模型models，包括：图像分类、性别判断、年龄估算；三个模型相关文件

高光谱图像分离matlab代码深度生成端元建模：无监督光谱解混的应用 这个包包含作者对论文 [1] 的实现。 为了解决光谱分离中的端元可变性，端元光谱使用深度生成模型 (VAE) 建模，该模型从观察到的高光谱图像中学习。 这使我们能够使用生成模型的低维潜在空间中的点对可变端元进行参数化，然后可以与丰度同时优化以解决分离问题。 代码在 MATLAB 中实现，包括： example1.m - 比较算法的演示脚本 (DC1) example2.m - 比较算法的演示脚本 (DC2) example3.m - 比较算法的演示脚本 (DC3) example4.m - 比较算法的演示脚本 (DC4) example_real1.m - 比较算法的演示脚本（休斯顿） example_real2.m - 比较算法的演示脚本 (Samson) example_real3.m - 比较算法的演示脚本（Jasper Ridge） ./DeepGUn/ - 包含与 DeepGUn 算法相关的 MATLAB 文件 ./python/ - 包含与 DeepGUn 算法相关的 Python 文件 ./other_

pactools入门 该软件包提供了用于估计神经时间序列中的相幅耦合（PAC）的工具。 特别是，它实现了以下参考文献中介绍的驱动自回归（DAR）模型[ ]。 在阅读更多。 安装 要安装pactools ，请使用以下两个命令之一： 最新稳定版本： pip install pactools 开发版本： pip install git+https://github.com/pactools/pactools.git#egg=pactools 要升级，请使用pip提供的--upgrade标志。 要检查一切是否正常，您可以执行以下操作： python -c 'import pactools' 并且不应给出任何错误消息。 相幅耦合（PAC） 迄今为止，在不同类别的交叉频率耦合中，相位幅度耦合（PAC）（即，时间锁定到慢频率振荡的特定相位的高频活动）是最公认的。 PAC通常用协调制

超越具体计算机语言本身，探讨多种范式的程序设计，比SICP更全面。

Introduction to Probability Models 11Edition(英文完整原版) ，（美）SHELDON M.ROSS著

ssd_mobilenet_v3_small_coco_2020_01_14.tar.gz，tensorflow/models下下载的预训练模型。

Concepts,Techniques,and Models of Computer Programming.pdf

Qt官方文档解析到提供支持类型QStringList, a QVariantList, a QObjectList or a QAbstractItemModel. 其中QAbstractItemModel提供复杂的数据模型． ​该例程是QAbstractItemModel与qml结合是怎么使用．

I will first introduce the necessary concepts and tools of verification, then I'll describe a process for planning and carrying out an effective functional verification of a design. I will also introduce the concept of coverage models that can be used in a coveragedriven verification process. It will be necessary to cover some VHDL and Verilog language semantics that are often overlooked or oversimplified in textbooks intent on describing the synthesizeable subset. These unfamiliar semantics become important in understanding what makes a wellimplemented and robust testbench and in providing the necessary control and monitor features. Once these new semantics are understood in a familiar language, the same semantics are presented in new verification-oriented languages. I will also present techniques for applying stimulus and monitoring the response of a design, by abstracting the physical-level transac-tions into high-level procedures using bus-functional models. The architecture of testbenches built around these bus-functional models is important to create a layer of abstraction relevant to the function being verified and to minimize development and maintenance effort. I also show some strategies for making testbenches selfchecking. Creating random testbenches involves more than calling the random() function in whatever language is used to implement them. I will show how random stimulus generators, built on top of busfunctional models, can be architected and designed to be able to produce the desired stimulus patterns. Random generators must be easily externally constrained to increase the likelihood that a set of interesting patterns will be generated. Behavioral modeling is another important concept presented in this book. It is used to parallelize the implementation and verification of a design and to perform more efficient simulations. For many, behavioral modeling is synonymous with synthesizeable or RTL modeling. In this book, the term "behavioral" is used to

Computational and Symbolic Models for Secure Computation.pdf