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本文是LLM系列文章，针对《Detoxifying Large Language Models via Knowledge Editing》的翻译。
本文研究了使用知识编辑技术对大型语言模型（LLM）进行解毒。我们构建了一个基准，即SafeEdit，它涵盖了九个不安全的类别，并提供了各种强大的攻击提示，并为系统评估提供了全面的指标。我们对几种知识编辑方法进行了实验，表明知识编辑有可能有效地解毒LLM，但对总体性能的影响有限。然后，我们提出了一个简单而有效的基线，称为术中神经监测排毒（DINM），仅通过一个实例在几个调整步骤内降低LLM的毒性。我们进一步深入分析了各种解毒方法的内在机制，表明以前的方法，如SFT和DPO，可能只是抑制毒性参数的激活，而DINM在一定程度上减轻了毒性参数的毒性，并进行了永久性调整。我们希望这些见解能为未来开发解毒方法和LLM的潜在知识机制的工作提供线索。
LLM Security and Privacy
LLM Editing
ArcGIS基础：CAD数据转换为GIS数据（符号化显示、数据转换、线转面、空间连接）
1、符号化显示： CAD数据加载进来有很多列表，包含了点【point】、线【polyline】、面【polygon】、标注数据【Annotation】等数据，本次实验我们需要用到的是面数据，也就是.dwgPolygong格式的数据。如下图所示： 把数据加载进来，然后在2处打开属性表，查看CAD数据的属性表，打开后可以发现【layer】图层表示就是地块的用地性质，它就是符号化显示的依据。 我们在数据上右键【属性】，打开图层属性，找到【符号系统】，选择类别中的【唯一值】，然后在【值字段】下拉选择【layer】，
别再只盯着YOLOv8了！手把手带你用PaddleDetection的PP-YOLOE+搞定你的第一个检测项目
本文介绍了PP-YOLOE+在目标检测领域的优势与应用实践，对比YOLOv8展示其在精度、速度和部署便利性上的突破。通过详细的代码示例和实战教程，帮助开发者快速上手PaddleDetection工具链，实现产业级目标检测项目，提升开发效率。
第五节 Amesim《直动型溢流阀HCD建模》HCD5 学习资料
利用HCD原则创建直动型溢流阀的界面。这可能包括图形化表示直动型溢流阀的工作原理、操作方式和调节方法。这样的界面应当直观且易于理解，使用户能够轻松了解阀门的功能和使用方法。
直线型一阶倒立摆1---从理论到实践的桥梁
本文深入探讨了直线型一阶倒立摆在控制理论中的核心地位，从非线性控制、鲁棒性到实时反馈等关键问题。通过实际案例和硬件搭建指南，帮助读者理解从理论到实践的过渡，特别适合控制工程初学者和研究人员。文章还提供了算法选型建议和硬件避坑手册，是掌握倒立摆控制的实用指南。
YOLOv11 改进 - 注意力机制 | EMA (Efficient Multi-Scale Attention) 高效多尺度注意力：跨空间学习与多分支协同增强特征表征，优化多尺度目标检测
本文介绍了高效多尺度注意力（EMA）模块及其在YOLOv11中的结合应用。现有注意力机制在通道维度缩减时可能影响深度视觉表示，EMA模块通过结合通道和空间信息、采用多尺度并行子网络结构等创新点，实现了高效的多尺度注意力机制。其基本原理包括通道和空间注意力结合、多尺度并行子网络设计等。在多个流行基准数据集上的实验表明，该模块能有效提高特征表示能力。我们将EMA集成进YOLOv11，实验证明改进后的模型在目标检测任务中性能优越，具有广泛应用前景
重回童年的经典系列☀️|【炸弹人小游戏】制作过程+解析 | 收藏起来跟曾经的小伙伴一起梦回童年！
Unity，小游戏开发。 本篇文章对童年经典小游戏——炸弹超人进行制作和解析。不知道有没有勾起你童年的回忆呢！快来和我一起制作这个童年经典的小游戏吧！ 敬请品尝！
【RT-DETR有效改进】计算训练好权重文件对应的FPS、推理每张图片的平均时间（科研必备）
本文给大家带来的改进机制是利用我们训练好的权重文件计算FPS，同时打印每张图片所利用的平均时间，模型大小（以MB为单位），同时支持batch_size功能的选择，对于轻量化模型的读者来说，本文的内容对你一定有帮助，可以清晰帮你展示出模型速度性能的提升以及轻量化的效果（模型大小），对于以提高精度为目的的读者本文也能够帮助大家展示出现阶段的模型速度指标。所以本文的内容是十分有用的机制，对于大家发表论文来说，下面的图片为运行后的效果可以看到，该有的指标均已打印（本文内容为我独家创新，全网无第二份）下面是效果图和文
HTML炫酷烟花④（完整代码）
HTML语言实现炫酷烟花的完整代码。
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！
Statistics and Machine Learning Toolbox
Statistics and Machine Learning Toolbox（带书签），MATLAB统计学习工具
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
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光步前台收银系统系统功能：商品销售、商品退货、挂单、收银员权限、会员销售、单品取消、整单取消、商品查询、脱机销售、脱机数据上传，基本资料下载，开钱箱、小票打印、收款结算，支持多币种付款方式（现金、储值卡、礼券、微信等）,支持多种促销。运行环境：.net 4.0登陆信息用户名:1001 密码:123456内置商品信息，商品编码可输入00001000 - 00010000,测试效果相关功能键F5 结算 F9挂单 F11商品查询 F8退货 Insert单品取消 Del整单取消 F12开钱箱F2 会员 Ctrl+Z退出系统 Home 锁屏 相关源码待公开
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