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kapt是kotlin的注解处理器，用法很简单
在 Kotlin 中则需要添加 kotlin-kapt 插件激活 kapt，并使用 kapt 替换 annotationProcessor：
配置 在app build.gradle 中 激活
apply plugin: 'kotlin-kapt'
使用
dependencies {
...
kapt "com.google.dagger:dagger-compiler:$dagger-version"
}
ok，这样就完成了kotlin中的kapt的应用了。
Kapt篇: Android编译优化系列
通过使用Kapt，我们可以在编译期间自动处理注解，并生成额外的代码。在dependencies块中，我们添加了一个库的依赖项，并使用kapt关键字添加了一个注解处理器的依赖项。注意注解处理器的版本兼容性：由于Kapt是Kotlin编译器的一部分，所以需要确保使用的注解处理器与项目中使用的Kotlin版本兼容。运行编译命令后，Kapt将自动扫描和处理代码中的注解，并执行我们在注解处理器中定义的逻辑。当我们编译项目时，Kapt将会扫描代码中的注解，并将其传递给注解处理器进行处理。假设我们有一个自定义的注解。
Kotlin里kapt引入 的报错
原因：kotlin开发的插件不支持跨module，所以databinding在使用apt技术BR文件时的引用没发确定目录，故造成unresolved reference：BR，那么需要kapt完成。新问题：Class not found 自定义生成的类 生成不了。日志：unresolved reference: BR。原因：已经kapt了 就要换成kapt。最开始遇到的问题是 BR文件 找不到。...
org/jetbrains/kotlin/kapt/idea/KaptGradleModelorg/jetbrains/kotlin/kapt/idea/KaptGradleModel
Gradle sync failed: org/jetbrains/kotlin/kapt/idea/KaptGradleModelorg/jetbrains/kotlin/kapt/idea/KaptGradleModel**加粗样式** ```缩写。在 Kotlin 中通过kapt编译器插件支持注解处理器(参见JSR 269)。简而言之，你可以在 Kotlin 项目中使用像Dagger或者Data Binding这样的库。在 Gradle 中使用应用kotlin-kaptGradle 插件：使用apply pl...
仿真模型简单介绍
同时，各种以伯克利的Spice仿真程序的算法为核心的商用Spice电路仿真工具也随之产生，运行在PC和UNIX平台，许多都是基于原始的SPICE2G6版的源代码，这是一个公开发表的版本，它们都在Spice的基础上做了很多实用化的工作，比较常见的Spice仿真软件有HSpice、Pspice、Spectre、Tspice、SmartSpcie、IsSpice等，虽然它们的核心算法雷同，但仿真速度、精度和收敛性却不一样，其中以Synopsys公司的Hspice和Cadence公司的Pspice最为著名。...
存储硬盘格式怎么把512还原为520格式??
硬盘厂商工具（如SeaChestsg_format）可以调整扇区大小。备份数据，因为调整扇区会清除硬盘内容。根据硬盘型号和接口类型，选择合适的工具进行调整。如果你需要更详细的帮助，请提供你的硬盘型号和存储设备类型。希望如上措施及解决方案能够帮到有需要的你。PS：如若遇到采纳如下方案还是未解决的同学，希望不要抱怨&&急躁，毕竟影响因素众多，我写出来也是希望能够尽最大努力帮助到同类似问题的小伙伴，即把你未解决或者产生新Bug黏贴在评论区，我们大家一起来努力，一起帮你看看，可以不咯。
Android--Gradle DSL method not found: ‘kapt()‘
APP级的build.gradle文件中添加 apply plugin: 'kotlin-kapt'
VC中sqlite的使用
在VC中对sqlite的使用，实现数据库大批数据的快速插入，读取数据库中的数据在ListCtrl中显示
Could not find method kapt() for arguments
使用kapt注解提示Could not find method kapt() for arguments 这是因为没有引用kapt 在相应模块build.gradle添加 如 apply plugin: 'kotlin-android' apply plugin: 'kotlin-kapt' dependencies { kapt 'com.jakewharton:butterknife-compiler:8.4.0'' } 注 kapt 可以替代annotationProcessor
老版QQ原生表情gif动态
这是老版的QQ表情，不过全是动态的的，Windows照片查看器是不会动的，可以右击用浏览器打开查看，再说一遍是老版的，只是分享给宁愿用老版也要动态的人。
深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[](https://bitdeli.com/free "Bitdeli Badge")
CVE-2024-1086 Linux 本地提权：普通账户三分钟拿到 root
CVE-2024-1086 是 Linux 内核 nf_tables 模块的本地提权漏洞，受影响范围为 v5.14 至 v6.6（已排除修复的 stable 分支）。在未打补丁的系统上，任意普通账户无需密码即可提升至 root，PoC 在 KernelCTF 镜像中的成功率为 99.4%。运行 Debian、Ubuntu 及同类发行版的运维人员应优先排查。 ## 3 分钟自查：你的内核是否已修复
数学建模模型总结
这是本人在两年内准备参加数学建模竞赛搜集到的模型资料，已经经过了本人的筛选和整理，希望能够对准备参加数学建模竞赛的学弟学妹们提供一些帮助！
Android apt被注解为Kotlin写的代码时不能自动生成文件bug
记录一下自己遇到的问题， 写了个类似Butterknife，通过注解可以自动生成代码的逻辑，但是在运行的时候发现并没有生成相关文件。 具体解释一下， （1）自己写了一个类似ButterKnife可以在编译时生成对应文件的apt（Annotation processor tool 注解处理器） （2）注解在一个Kt文件上，编译后没有自动生成对应的文件，但是注解在java文件上就可以生成对应的文件。 Demo 链接 原因： 涉及到注解的注解处理器依赖也会由annotationProcessor替换成kapt 解
as编译的报错提示：cannot find implementation for 包名.db.xxDatabase. xxDatabase_Impl does not exist
gradle
Arouter 在Kotlin与Java混编中的跳转配置
Arouter 在kotlin和java混编中跳转配置
从0到1实现一个Android路由(5)——对Kotlin模块的支持
在从0到1实现一个Android路由(3)——APT收集路由中，提到过Processor处理Kotlin类时，无法得到注解从而路由表不正确，本章将介绍如何解决这个问题。 参考自阿里的ARouter。 解决方案 其实解决方案很简单，修改在有用注解标注的Kotlin写的Activity的module的build.gradle。修改点有几点： 添加apply plugin:‘kapt’ 将annota...
android databinding 找不到BR
AndroidStudio：3.3.12 在Gradle Scripts里的build.gradle(Module:app)里首部添加 apply plugin: 'kotlin-kapt' kapt 即 Kotlin annotation processing tool（Kotlin 注解处理工具）缩写。 在 Kotlin 中通过 kapt 编译器插件支持注解处理器（参见JSR 269...
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