Linux distribution

A Linux distribution (often abbreviated as distro) is an operating system made from a software collection that includes the Linux kernel, and often a package management system. Linux users usually obtain their operating system by downloading one of the Linux distributions, which are available for a wide variety of systems ranging from embedded devices (for example, OpenWrt) and personal computers (for example, Linux Mint) to powerful supercomputers (for example, Rocks Cluster Distribution). A typical Linux distribution comprises a (usually modified) Linux kernel, an init system (such as systemd, OpenRC, or runit), GNU tools and libraries, documentation, and many other software (such as IP network configuration utilities and the getty TTY setup program, among others). Optionally, to provide a desktop experience (most commonly the Mesa userspace graphics drivers) a display server (the most common being the X.org Server, or, more recently, a Wayland compositor such as Sway, KDE's KWin, or GNOME's Mutter), a desktop environment, a sound server (usually either PulseAudio or more recently PipeWire), and other related programs may be included with the distribution or are installable by the user. Most of the included software is free and open-source software made available both as compiled binaries and in source code form, allowing modifications to the original software. Usually, Linux distributions optionally include some proprietary software that may not be available in source code form, such as binary blobs required for some device drivers. A Linux distribution may also be described as a particular assortment of application and utility software (various GNU tools and libraries, for example), packaged with the Linux kernel in such a way that its capabilities meet many users' needs. The software is usually adapted to the distribution and then combined into software packages by the distribution's maintainers. The software packages are available online in repositories, which are storage locations usually distributed around the world.

Match Normalization: Learning-Based Point Cloud Registration for 6D Object Pose Estimation in the Real World

Mathieu Salzmann, Zheng Dang, Yu Guo

In this work, we tackle the task of estimating the 6D pose of an object from point cloud data. While recent learning-based approaches have shown remarkable success on synthetic datasets, we have observed them to fail in the presence of real-world data. We ...

Ieee Computer Soc2024

Center-aware Adversarial Augmentation for Single Domain Generalization

Mathieu Salzmann, Zhiye Wang

Domain generalization (DG) aims to learn a model from multiple training (i.e., source) domains that can generalize well to the unseen test (i.e., target) data coming from a different distribution. Single domain generalization (SingleDG) has recently emerge ...

IEEE COMPUTER SOC2023

Match Normalization: Learning-Based Point Cloud Registration for 6D Object Pose Estimation in the Real World

Center-aware Adversarial Augmentation for Single Domain Generalization

Monte-Carlo Method for EMF Exposure Distribution Assessment in 5G Base Station

Match Normalization: Learning-Based Point Cloud Registration for 6D Object Pose Estimation in the Real World

Center-aware Adversarial Augmentation for Single Domain Generalization

Monte-Carlo Method for EMF Exposure Distribution Assessment in 5G Base Station