t-BNE: Tensor-based Brain Network Embedding

go-flutter-将Flutter带到桌面的软件包 目的 允许您从一个代码库在iOS和Android上构建漂亮的本机应用程序。 这个项目通过和的功能将Flutter带到了桌面。 颤振引擎本身不知道如何处理桌面平台（例如，处理鼠标/键盘输入） 。 相反，它为要实现的任何平台公开了一个抽象层。 该项目使用在Windows，macOS和Linux上运行的单个代码库实现 。 对于渲染， 适合该工作，因为它为每个平台提供了OpenGL的Buffer / Mouse / Keyboard上正确的抽象。 的选择来自于每个平台上具有相同工具的事实。 另外，Golang是一种很棒的语言，因为它使所有内容都简单易读，这使得构建跨平台插件变得容易。 入门 最好的入门方法是安装 ，这是官方的Flutter工具，用于在桌面上设置，构建和运行Flutter应用程序，包括热重装。 阅读以在桌面上运行您的应用，或从开始。 如果您想了解有关颤振的更多详细信息，请阅读 。 支持的功能 Linux :penguin: 苹果系统 :red_apple: 视窗 :chequered_flag: 插件系统 B

论文翻译，保留原文排版。 SphereFace: Deep Hypersphere Embedding for Face Recognition

Reversible Data Embedding Using a Difference Expansion算法matlab实现

Abstract—Although there has been substantial research in software analytics for effort estimation in traditional software projects, little work has been done for estimation in agile projects, especially estimating user stories or issues. Story points are the most common unit of measure used for estimating the effort involved in implementing a user story or resolving an issue. In this paper, we offer for the first time a comprehensive dataset for story points-based estimation that contains 23,313 issues from 16 open source projects. We also propose a prediction model for estimating story points based on a novel combination of two powerful deep learning architectures: long short-term memory and recurrent highway network. Our prediction system is endto- end trainable from raw input data to prediction outcomes without any manual feature engineering. An empirical evaluation demonstrates that our approach consistently outperforms three common effort estimation baselines and two alternatives in both Mean Absolute Error and the Standardized Accuracy.