Mastodawn

@stefan_hessbrueggen I am not in the know about the GNOME/libxml2 developments, but a while ago a blogpost by @faassen got me curious. Took me a while to find it again, but here it is: https://blog.startifact.com/posts/xee/

#Xee is an almost complete #XPath31 implementation plus incomplete #XSLT3 inplementation, in #Rust. Here is the repo (last commit from Oct 2025): https://github.com/Paligo/xee

Probably something to keep an eye on...

Xee: A Modern XPath and XSLT Engine in Rust

I announce Xee, the implementation of XPath and XSLT in Rust that I've been working on for the last two years.

Secret Weblog

Bojidar Marinov Aug 29, 2025

I got beaten to posting my own article, but here it is again with a few more hashtags: 😁😅

https://bojidar-bg.dev/blog/2025-08-28-joining-xee/

#100DaysToOffload #xee #opensource

Joining forces on Xee

I'm going to be contributing to the XSLT side of the Xee library!

bojidar-bg.dev

Hemera

Apr 8, 2025

The fact that XPath has support to serialize to JSON opens up a lot of amazing opportunities

👀

#xml #xpath #xee #rust #rustlang

N-gated Hacker News Mar 28, 2025

🔥 Hold onto your hats, nerds! 🤓 #Xee is here to revolutionize the thrilling world of #XML with a Rust-powered engine that no one asked for. Apparently, Martijn Faassen spent two years crafting this masterpiece, because what could be more exhilarating than reinventing the wheel with #programming languages nobody wants to remember? 😂🚀
https://blog.startifact.com/posts/xee/ #Rust #Revolution #Humor #Tech #News #HackerNews #ngated

Xee: A Modern XPath and XSLT Engine in Rust

I announce Xee, the implementation of XPath and XSLT in Rust that I've been working on for the last two years.

Secret Weblog

Hacker News Mar 28, 2025

Xee: A Modern XPath and XSLT Engine in Rust

https://blog.startifact.com/posts/xee/

#HackerNews #Xee #XPath #XSLT #Rust #Programming #ModernTech

Xee: A Modern XPath and XSLT Engine in Rust

I announce Xee, the implementation of XPath and XSLT in Rust that I've been working on for the last two years.

Secret Weblog

Alexander Deplov 🇩🇪Sep 5, 2024

I used to save images from web that inspires me, which I can use later for inspiration for my work at a folder in Finder.

I also install image viewer #Xee to browse them directly from folder. But damn it, Xee can't handle so many formats, even JPG sometimes can't be shown.

AOSbot Jun 18, 2023

Title: Graph-Based Matrix Completion Applied to Weather Data.

Low-rank matrix completion is the task of recovering unknown entries of a
matrix by assuming that the true matrix admits a good low-rank approximation.
Sometimes additional information about the variables is known, and
incorporating this information into a matrix completion model can lead to a
better completion quality. We c [...]

Authors: Benoît Loucheur, P.-A. Absil, Michel Journée

Link: http://arxiv.org/abs/2306.08627

Graph-Based Matrix Completion Applied to Weather Data

Low-rank matrix completion is the task of recovering unknown entries of a matrix by assuming that the true matrix admits a good low-rank approximation. Sometimes additional information about the variables is known, and incorporating this information into a matrix completion model can lead to a better completion quality. We consider the situation where information between the column/row entities of the matrix is available as a weighted graph. In this framework, we address the problem of completing missing entries in air temperature data recorded by weather stations. We construct test sets by holding back data at locations that mimic real-life gaps in weather data. On such test sets, we show that adequate spatial and temporal graphs can significantly improve the accuracy of the completion obtained by graph-regularized low-rank matrix completion methods.

arXiv.org

AOSbot Jun 17, 2023

Title: Graph-Based Matrix Completion Applied to Weather Data.

Low-rank matrix completion is the task of recovering unknown entries of a
matrix by assuming that the true matrix admits a good low-rank approximation.
Sometimes additional information about the variables is known, and
incorporating this information into a matrix completion model can lead to a
better completion quality. We c [...]

Authors: Benoît Loucheur, P.-A. Absil, Michel Journée

Link: http://arxiv.org/abs/2306.08627

Graph-Based Matrix Completion Applied to Weather Data

Low-rank matrix completion is the task of recovering unknown entries of a matrix by assuming that the true matrix admits a good low-rank approximation. Sometimes additional information about the variables is known, and incorporating this information into a matrix completion model can lead to a better completion quality. We consider the situation where information between the column/row entities of the matrix is available as a weighted graph. In this framework, we address the problem of completing missing entries in air temperature data recorded by weather stations. We construct test sets by holding back data at locations that mimic real-life gaps in weather data. On such test sets, we show that adequate spatial and temporal graphs can significantly improve the accuracy of the completion obtained by graph-regularized low-rank matrix completion methods.

arXiv.org

AOSbot Jun 16, 2023

Title: Graph-Based Matrix Completion Applied to Weather Data.

Low-rank matrix completion is the task of recovering unknown entries of a
matrix by assuming that the true matrix admits a good low-rank approximation.
Sometimes additional information about the variables is known, and
incorporating this information into a matrix completion model can lead to a
better completion quality. We c [...]

Authors: Benoît Loucheur, P.-A. Absil, Michel Journée

Link: http://arxiv.org/abs/2306.08627

Graph-Based Matrix Completion Applied to Weather Data

Low-rank matrix completion is the task of recovering unknown entries of a matrix by assuming that the true matrix admits a good low-rank approximation. Sometimes additional information about the variables is known, and incorporating this information into a matrix completion model can lead to a better completion quality. We consider the situation where information between the column/row entities of the matrix is available as a weighted graph. In this framework, we address the problem of completing missing entries in air temperature data recorded by weather stations. We construct test sets by holding back data at locations that mimic real-life gaps in weather data. On such test sets, we show that adequate spatial and temporal graphs can significantly improve the accuracy of the completion obtained by graph-regularized low-rank matrix completion methods.

arXiv.org