Summary of the de-embedding methods 去嵌入总结.pdf

Open Source Intelligence Methods and Tools focuses on building a deep understanding of how to exploit open source intelligence (OSINT) techniques, methods, and tools to acquire information from publicly available online sources to support intelligence analysis. The harvested data can be used in different scenarios such as financial, crime,and terrorism investigations as well as in more regular tasks such as analyzing business competitors, running background checks, and acquiring intelligence about individuals and other entities. This book will also improve your skills in acquiring information online from the surface web, the deep web, and the darknet. Many estimates show that 90 percent of useful information acquired by intelligence services comes from public sources (in other words, OSINT sources). Social media sites open up numerous opportunities for investigations because of the vast amount of useful information located in one place. For example, you can get a great deal of personal information about any person worldwide by just checking their Facebook page. This book will show you how to conduct advanced social media investigations to access content believed to be private, use advanced search engines queries to return accurate results, search historical deleted versions of websites, track individuals online using public record databases and people-searching tools, locate information buried in the deep web, access and navigate the dark web, collect intelligence from the dark web, view multiple historic satellite images and street views of any location, search geolocation information within popular social media sites, and more. In short, you will learn how to use a plethora of techniques, tools, and free online services to gather intelligence about any target online. OSINT-gathering activities should be conducted secretly to avoid revealing the searcher’s identity. Therefore, this book will teach you how to conceal your digital identity and become anonymous online

本教程介绍了经济理论中使用的基本数学工具。 假定掌握基本演算。

有限元方法与应用，适合有一定基础的有限元朋友，国外流行的有限元教程。

vue的基本选项的作用

IEC 62552-1_3：2020 Household refrigerating appliances - Characteristics and test methods.7z

IEC 60749-1_44 Semiconductor devices – Mechanical and climatic test methods -包含全部45份最新英文版标准文件.7z

ISO 11452(1-11) Road vehicles — Component test methods for electrical disturbances from narrowband radiated electromagnetic energy - 完整英文版（1-11共10份文件）.7z

Graph Neural Networks: Methods, Applications, and Opportunities 在过去十年左右的时间里，我们见证了深度学习重振机器学习领域。它以最先进的性能解决了计算机视觉、语音识别、自然语言处理和各种其他任务领域的许多问题。数据通常在这些域中的欧几里得空间中表示。各种其他域符合非欧几里得空间，图是其中的理想表示。图适用于表示各种实体之间的依赖关系和相互关系。传统上，图形的手工特征无法从这种复杂的数据表示中为各种任务提供必要的推理。最近，出现了利用深度学习中的各种进步来绘制基于数据的任务的趋势。本文对每个学习设置中的图神经网络 (GNN) 进行了全面调查：监督学习、无监督学习、半监督学习和自监督学习。每个基于图的学习设置的分类都提供了属于给定学习设置的方法的逻辑划分。从理论和经验的角度分析每个学习任务的方法。此外，我们提供了构建 GNN 的通用架构指南。还提供了各种应用程序和基准数据集，以及仍然困扰 GNN 普遍适用性的开放挑战。

许多学习任务需要处理包含丰富元素之间关系信息的图形数据。物理系统建模、学习分子指纹、预测蛋白质界面和疾病分类需要一个模型来从图形输入中学习。在其他领域，例如从文本和图像等非结构数据中学习，对提取的结构（如句子的依赖树和图像的场景图）进行推理是一个重要的研究课题，也需要图推理模型。图神经网络 (GNN) 是神经模型，它通过图节点之间的消息传递来捕获图的依赖性。近年来，图卷积网络 (GCN)、图注意力网络 (GAT)、图循环网络 (GRN) 等 GNN 的变体在许多深度学习任务上都表现出了突破性的表现。在本次调查中，我们为 GNN 模型提出了一个通用的设计流程，并讨论了每个组件的变体，系统地对应用程序进行了分类，并为未来的研究提出了四个开放性问题。