可以获得，生成，存储和管理的前所未有的卷，多样性和丰富的航空数据，提供了与航空相关行业的独特功能，并根据采用创新的大数据分析技术提供了仍有待解锁的价值。尽管对研究和创新的努力和投资，但大数据技术对其采用者造成了许多挑战。除了有效的存储和访问底层大数据外，应考虑有效的数据集成和数据互操作性，而同时应该通过执行不同利益相关者之间的数据交换和数据共享来有效地组合多个数据源。但是，这揭示了对收集数据的信息安全性的重要保存的额外挑战，可信和安全数据交换和数据共享以及强大的数据访问控制。目前的纸张旨在介绍ICARUS大数据的平台，目标提供了一个多面平台，提供了一个新颖的航空数据和智能市场，伴随着可信赖和安全的分析工作空间。它从数据收集，数据策择和数据探索到源自具有不同速度，品种和体积的数据集成和数据分析，从数据收集，数据策择和数据探索，以可信赖和安全的方式处理源自不同速度，品种和体积的数据的数据集成和数据分析。

航空工业以及福利和与其相关的行业是在大数据分析的形式中创新的成熟。可用大数据技术的数量不断增长，而现有特征的同时则同时迅速发展并赋予授权。然而，大数据时代强加了如何在管理来自异构数据源的大规模和快速发展的数据的同时有效处理信息安全的关键挑战。虽然已经出现了多种技术，但需要在大型安全要求，隐私义务，系统性能和大型数据集的快速动态分析之间找到平衡。目前的纸张旨在介绍ICarus平台的ICARUS安全实验沙箱。 ICARUS平台旨在提供一个大型数据的平台，旨在成为航空数据和情报市场的“一站式商店”，提供了一个值得信赖和安全的“沙箱”分析工作空间，允许探索，集成和深度分析原始和衍生数据以可靠和公平的方式。在此目的，在ICARUS平台产品中设计并集成了一个安全的实验沙箱，可以提供能够完全保证数据安全性和保密性的复杂环境，允许任何涉及的律师利用平台进行分析的平台闭合实验室条件下的实验。

Automatic differentiation (AD) is a technique for computing the derivative of a function represented by a program. This technique is considered as the de-facto standard for computing the differentiation in many machine learning and optimisation software tools. Despite the practicality of this technique, the performance of the differentiated programs, especially for functional languages and in the presence of vectors, is suboptimal. We present an AD system for a higher-order functional array-processing language. The core functional language underlying this system simultaneously supports both source-to-source forward-mode AD and global optimisations such as loop transformations. In combination, gradient computation with forward-mode AD can be as efficient as reverse mode, and the Jacobian matrices required for numerical algorithms such as Gauss-Newton and Levenberg-Marquardt can be efficiently computed.

A methodology is proposed, which addresses the caveat that line-of-sight emission spectroscopy presents in that it cannot provide spatially resolved temperature measurements in nonhomogeneous temperature fields. The aim of this research is to explore the use of data-driven models in measuring temperature distributions in a spatially resolved manner using emission spectroscopy data. Two categories of data-driven methods are analyzed: (i) Feature engineering and classical machine learning algorithms, and (ii) end-to-end convolutional neural networks (CNN). In total, combinations of fifteen feature groups and fifteen classical machine learning models, and eleven CNN models are considered and their performances explored. The results indicate that the combination of feature engineering and machine learning provides better performance than the direct use of CNN. Notably, feature engineering which is comprised of physics-guided transformation, signal representation-based feature extraction and Principal Component Analysis is found to be the most effective. Moreover, it is shown that when using the extracted features, the ensemble-based, light blender learning model offers the best performance with RMSE, RE, RRMSE and R values of 64.3, 0.017, 0.025 and 0.994, respectively. The proposed method, based on feature engineering and the light blender model, is capable of measuring nonuniform temperature distributions from low-resolution spectra, even when the species concentration distribution in the gas mixtures is unknown.

The combination of artist-curated scans, and deep implicit functions (IF), is enabling the creation of detailed, clothed, 3D humans from images. However, existing methods are far from perfect. IF-based methods recover free-form geometry but produce disembodied limbs or degenerate shapes for unseen poses or clothes. To increase robustness for these cases, existing work uses an explicit parametric body model to constrain surface reconstruction, but this limits the recovery of free-form surfaces such as loose clothing that deviates from the body. What we want is a method that combines the best properties of implicit and explicit methods. To this end, we make two key observations: (1) current networks are better at inferring detailed 2D maps than full-3D surfaces, and (2) a parametric model can be seen as a "canvas" for stitching together detailed surface patches. ECON infers high-fidelity 3D humans even in loose clothes and challenging poses, while having realistic faces and fingers. This goes beyond previous methods. Quantitative, evaluation of the CAPE and Renderpeople datasets shows that ECON is more accurate than the state of the art. Perceptual studies also show that ECON's perceived realism is better by a large margin. Code and models are available for research purposes at https://xiuyuliang.cn/econ

As a result of the ever increasing complexity of configuring and fine-tuning machine learning models, the field of automated machine learning (AutoML) has emerged over the past decade. However, software implementations like Auto-WEKA and Auto-sklearn typically focus on classical machine learning (ML) tasks such as classification and regression. Our work can be seen as the first attempt at offering a single AutoML framework for most problem settings that fall under the umbrella of multi-target prediction, which includes popular ML settings such as multi-label classification, multivariate regression, multi-task learning, dyadic prediction, matrix completion, and zero-shot learning. Automated problem selection and model configuration are achieved by extending DeepMTP, a general deep learning framework for MTP problem settings, with popular hyperparameter optimization (HPO) methods. Our extensive benchmarking across different datasets and MTP problem settings identifies cases where specific HPO methods outperform others.

Climate change is expected to aggravate wildfire activity through the exacerbation of fire weather. Improving our capabilities to anticipate wildfires on a global scale is of uttermost importance for mitigating their negative effects. In this work, we create a global fire dataset and demonstrate a prototype for predicting the presence of global burned areas on a sub-seasonal scale with the use of segmentation deep learning models. Particularly, we present an open-access global analysis-ready datacube, which contains a variety of variables related to the seasonal and sub-seasonal fire drivers (climate, vegetation, oceanic indices, human-related variables), as well as the historical burned areas and wildfire emissions for 2001-2021. We train a deep learning model, which treats global wildfire forecasting as an image segmentation task and skillfully predicts the presence of burned areas 8, 16, 32 and 64 days ahead of time. Our work motivates the use of deep learning for global burned area forecasting and paves the way towards improved anticipation of global wildfire patterns.

The current success of machine learning on image-based combustion monitoring is based on massive data, which is costly even impossible for industrial applications. To address this conflict, we introduce few-shot learning in order to achieve combustion monitoring and classification for the first time. Two algorithms, Siamese Network coupled with k Nearest Neighbors (SN-kNN) and Prototypical Network (PN), were tested. Rather than utilizing solely visible images as discussed in previous studies, we also used Infrared (IR) images. We analyzed the training process, test performance and inference speed of two algorithms on both image formats, and also used t-SNE to visualize learned features. The results demonstrated that both SN-kNN and PN were capable to distinguish flame states from learning with merely 20 images per flame state. The worst performance, which was realized by PN on IR images, still possessed precision, accuracy, recall, and F1-score above 0.95. We showed that visible images demonstrated more substantial differences between classes and presented more consistent patterns inside the class, which made the training speed and model performance better compared to IR images. In contrast, the relatively low quality of IR images made it difficult for PN to extract distinguishable prototypes, which caused relatively weak performance. With the entrire training set supporting classification, SN-kNN performed well with IR images. On the other hand, benefitting from the architecture design, PN has a much faster speed in training and inference than SN-kNN. The presented work analyzed the characteristics of both algorithms and image formats for the first time, thus providing guidance for their future utilization in combustion monitoring tasks.

我们建议使用两层机器学习模型的部署来防止对抗性攻击。第一层确定数据是否被篡改，而第二层解决了域特异性问题。我们探索三组功能和三个数据集变体来训练机器学习模型。我们的结果表明，聚类算法实现了有希望的结果。特别是，我们认为通过将DBSCAN算法应用于图像和白色参考图像之间计算的结构化结构相似性指数测量方法获得了最佳结果。

图像分类的深卷卷神经网络（CNN）依次交替交替进行卷积和下采样操作，例如合并层或陷入困境的卷积，从而导致较低的分辨率特征网络越深。这些降采样操作节省了计算资源，并在下一层提供了一些翻译不变性以及更大的接收领域。但是，这样做的固有副作用是，在网络深端产生的高级特征始终以低分辨率特征图捕获。逆也是如此，因为浅层总是包含小规模的特征。在生物医学图像分析中，工程师通常负责对仅包含有限信息的非常小的图像贴片进行分类。从本质上讲，这些补丁甚至可能不包含对象，而分类取决于图像纹理中未知量表的微妙基础模式的检测。在这些情况下，每一个信息都是有价值的。因此，重要的是要提取最大数量的信息功能。在这些考虑因素的推动下，我们引入了一种新的CNN体​​系结构，该体系结构可通过利用跳过连接以及连续的收缩和特征图的扩展来保留深，中间和浅层层的多尺度特征。使用来自胰腺导管腺癌（PDAC）CT扫描的非常低分辨率斑块的数据集，我们证明我们的网络可以超越最新模型的当前状态。