Overview Using Github Action push to another Repository is common in used. There are two ways to set up this GitHub action: Using SSH deploy keys (recommended, a bit harder to set up): push-to-another-repository-deploy-keys-example. The configuration is in the file ci.yml (ssh keys). Using a Personal Access Token (first iteration, not recommended but easier to set up: push-to-another-repository-example. The configuration is in the file ci.yml (token). Setup using the Personal Access Token This method is here for compatibility with the initial approach of this GitHub Action.
Introduction Overleaf is a collaborative cloud-based LaTeX editor used for writing, editing and publishing scientific documents. Using Git and GitHub Overleaf offers Git-Bridge and GitHub Synchronization features that allow you to link your Overleaf projects with local Git repositories or synchronize them with Git repositories hosted on GitHub. Overleaf Git-Bridge and GitHub Synchronization features allow you to work on your LaTeX source offline, help you to share with collaborators outside of Overleaf, and allow you to integrate Overleaf into more complex workflows.
Introduction Rust is a new systems programming language that combines the performance and low-level control of C and C++ with memory safety and thread safety. Rust’s modern, flexible types ensure your program is free of null pointer dereferences, double frees, dangling pointers, and similar bugs, all at compile time, without runtime overhead. In multi-threaded code, Rust catches data races at complie time, making concurrency much easier to use. Installing Rust Rustup: the Rust installer and version management tool.
Introduction Image classification Alexnet, VGG, Googlenet, Resnet, Densen, etc. Object detection/localization RCNN, FastRCNN, Faster RCNN, YOLO V1-V4, SSD, EffcientDet, etc. Semantic Segmentation FCN, segnet, Bisenet, Unet, etc. Instance Segmentation Combine Object Localization and Semantic segmentation MaskRCNN Comparison between image classification, object detection and instance segmentation. Object Detection Task: it is necessary to identify the categories of object and its position with a box. Classification Bounding-box Classification: Performance Metrics: Accuracy Score (0.
Introduction This article aims to assist users creating their own packages using the Arch Linux “ports-like” build system. It covers creation of a PKGBUILD – a package build description file sourced by makepkg to create a binary package from source. If already in possession of a PKGBUILD, see makepkg. For instructions regarding existing rules and ways to improve package quality see Arch packaging standards.
Introduction We will focus on general linear processes, moving average processes, autoregressive processes, the mixed Autoregressive Moving Average (ARMA) models and invertibility. These concepts provide the required basis for one of the frequently used ARMA methodology. We will have hands-on tasks to deepen your understanding of ARMA models and improve your skillsets for the implementation of time series analysis methods.