随机种子 成熟的随机数生成器库，提供 Xorshift、Xorwow、Mersenne Twister、PCG 和 LCG 的 32 位和 64 位高质量实现。 每个实现一个标准的 API，产生与原始实现完全匹配的数字分布。 强调 避免了困扰其他 javascript 实现的随机数生成器的状态溢出问题。 匹配所有算法的原始创作的 C/C++ 实现的输出。 32 位和 64 位生成器。 适用于所有生成器的简单、通用的 API。 光脚印。 浏览器支持。 ES 样式模块。 安装 npm install random-seedable --save 入门 只想轻松使用随机生成器？ 您所要做的就是导入 random，并像使用您自己初始化的生成器一样使用它。 只需导入 random 并调用您喜欢的任何方法， import random from 'random-seedable'

This book highlights state-of-the-art research on big data and the Internet of Things (IoT), along with related areas to ensure efficient and Internet-compatible IoT systems. It not only discusses big data security and privacy challenges, but also energy-efficient approaches to improving virtual machine placement in cloud computing environments. Big data and the Internet of Things (IoT) are ultimately two sides of the same coin, yet extracting, analyzing and managing IoT data poses a serious challenge. Accordingly, proper analytics infrastructures/platforms should be used to analyze IoT data. Information technology (IT) allows people to upload, retrieve, store and collect information, which ultimately forms big data. The use of big data analytics has grown tremendously in just the past few years. At the same time, the IoT has entered the public consciousness, sparking people’s imaginations as to what a fully connected world can offer. Further, the book discusses the analysis of real-time big data to derive actionable intelligence in enterprise applications in several domains, such as in industry and agriculture. It explores possible automated solutions in daily life, including structures for smart cities and automated home systems based on IoT technology, as well as health care systems that manage large amounts of data (big data) to improve clinical decisions. The book addresses the security and privacy of the IoT and big data technologies, while also revealing the impact of IoT technologies on several scenarios in smart cities design. Intended as a comprehensive introduction, it offers in-depth analysis and provides scientists, engineers and professionals the latest techniques, frameworks and strategies used in IoT and big data technologies. . Read more... Abstract: This book highlights state-of-the-art research on big data and the Internet of Things (IoT), along with related areas to ensure efficient and Internet-compatible IoT systems. It not only discusses big data security and privacy challenges, but also energy-efficient approaches to improving virtual machine placement in cloud computing environments. Big data and the Internet of Things (IoT) are ultimately two sides of the same coin, yet extracting, analyzing and managing IoT data poses a serious challenge. Accordingly, proper analytics infrastructures/platforms should be used to analyze IoT data. Information technology (IT) allows people to upload, retrieve, store and collect information, which ultimately forms big data. The use of big data analytics has grown tremendously in just the past few years. At the same time, the IoT has entered the public consciousness, sparking people’s imaginations as to what a fully connected world can offer. Further, the book discusses the analysis of real-time big data to derive actionable intelligence in enterprise applications in several domains, such as in industry and agriculture. It explores possible automated solutions in daily life, including structures for smart cities and automated home systems based on IoT technology, as well as health care systems that manage large amounts of data (big data) to improve clinical decisions. The book addresses the security and privacy of the IoT and big data technologies, while also revealing the impact of IoT technologies on several scenarios in smart cities design. Intended as a comprehensive introduction, it offers in-depth analysis and provides scientists, engineers and professionals the latest techniques, frameworks and strategies used in IoT and big data technologies.

This article has been accepted for inclusion in a future issue of this journal.

Eric 的 MagicaVoxel 着色器 又名 EMVS。 MagicaVoxel 的着色器，包括地形生成器、高级洪水系统等。 国际化 - 翻译，目标版本： 58 -通过翻译 -授权复制，目标版本： 0.0.7.0 （我正在寻找新的法语翻译。） 项目信息 当前版本： 59 在MIT License下MIT License 安装 将此项目中的shader shader目录中的 .txt 文件复制到您的 MagicaVoxel 安装的shader目录中。 兼容性 版本 兼容 笔记 0.99.5 及之前 EMVS 55 版本后不兼容 0.99.5.1 及之后 所有 EMVS 版本都兼容 路线图 云生成器 树随机化器。 调试 看到 。 着色器 从版本 55 开始，EMVS不再支持命令执行。 请通过 MagicaVoxel 0.99.5.1 以后提供的图形选项配置相关参数。 同时，由于

机器学习方法已经广泛应用于药物发现领域，使得更强大和高效的模型成为可能。在深度模型出现之前，建模分子在很大程度上是由专家知识驱动的;为了表现分子结构的复杂性，这些手工设计的规则被证明是不够的。深度学习模型是强大的，因为它们可以学习问题的重要统计特征——但只有正确的归纳偏差。我们在两个分子问题的背景下解决这个重要的问题:表征和生成。深度学习的典型成功在于它能够将输入域映射到有意义的表示空间。这对于分子问题尤其尖锐，分子之间的“正确”关系微妙而复杂。本论文的第一部分将重点讨论分子表征，特别是性质和反应预测。在这里，我们探索了一种用于分子表示的Transformer式架构，提供了将这些模型应用于图形结构对象的新工具。抛开传统的图神经网络范式，我们展示了分子表示原型网络的有效性，它允许我们对分子的学习性质原型进行推理。最后，我们在改进反应预测的背景下研究分子表示。本论文的第二部分将集中在分子生成，这是至关重要的药物发现作为一种手段，提出有前途的药物候选人。我们开发了一种新的多性质分子生成方法，通过首先学习分子片段的分布词汇。然后，利用这个词汇，我们调查了化学空间的有效探索方法。

NLP_pytorch_project 1-聊天机器人 001-transformer_chatbot 实现方式是标准的transformer。 002-bert_chatbot 参考UNILM 2嵌入 001-skipgram-word2vec.py 002-bert.py 003-albert.py 004-NPLM.py 3-NMT 001-transformer_NMT 002-gru_seq2seq_attention 003-lstm_seq2seq_attention 4文本分类 001-TextCNN.py 002-BILSTM+Attention.py 003-CharCNN 004-BERT_Classification 005-ERNIE_Classification 006-ALB

This text has been created to satisfy the growing demand for knowledge in two emerging areas: adaptive antennas (also known as smart antennas) and Code Division Multiple Access. CDMA was commercialized in the early 1990s by Qualcomm, Inc., a San Diego, California, company that pioneered the use of a classic military concept for the burgeoning cellular telephone industry. Adaptive arrays, first conceptualized in the 1960s with the birth of digital signal processing, only recently have become practical for deployment; the intense growth rates fo wireless services around the world are beckoning for their commercial use.

程序地图生成器 用于 roguelike 游戏的程序地图生成器。 ProcJam 2017 迟交。 由于图块集是受版权保护的内容，因此被排除在外。 截图 下载 (Windows)

APAP中采样算法参考文献

歌曲歌词数据集 数据： ： 使用LSTM并使用Word2vec进行分析的阿姆歌词 RNN（带反馈的神经网络）在NLP和语言建模中非常有用 递归神经网络也可以用作生成模型。 这意味着，除了用于预测模型（进行预测）之外，他们还可以学习问题的序列，然后为问题域生成全新的合理序列。 这样的生成模型不仅对研究模型学习问题的能力有用，而且对问题域本身也有更多的了解。 参考： : 埃德·希兰（Ed Sheeran）歌词参考（N Gram） n-gram模型广泛用于统计自然语言处理中。 在语音识别中，使用n元语法分布对音素和音素序列进行建模。 为了解析，对单词建模，以使每个n-gram由n个单词组成。 n-gram模型经常受到批评，因为它们缺乏任何对远程依赖的明确表示。 这是因为对于n元语法模型，唯一的显式依赖性范围是（n − 1）个标记，并且由于自然语言包含许多无界依赖性的情况（例如w