Robot Updated at:24 Feb 2025 21:11:15 GMT

yiliuyan161 · yiliuyan161 · commit 8f72707724e8 · 2025-02-24T21:11:15.000Z
diff --git a/docs/awesome/awesome-agi-cocosci.md b/docs/awesome/awesome-agi-cocosci.md
@@ -292,7 +292,7 @@ Contributions are greatly welcomed! Please refer to [Contribution Guidelines](ht
 
 * [Practical Bayesian Optimization of Machine Learning Algorithms](https://proceedings.neurips.cc/paper/2012/hash/05311655a15b75fab86956663e1819cd-Abstract.html) - ***NeurIPS'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=14442949298925775705)]. The use of machine learning algorithms frequently involves careful tuning of learning parameters and model hyperparameters. Unfortunately, this tuning is often a “black art” requiring expert experience, rules of thumb, or sometimes brute-force search. There is therefore great appeal for automatic approaches that can optimize the performance of any given learning algorithm to the problem at hand. This work considers this problem through the framework of Bayesian optimization, in which a learning algorithm’s generalization performance is modeled as a sample from a Gaussian process (GP). The authors show that certain choices for the nature of the GP, such as the type of kernel and the treatment of its hyperparameters, can play a crucial role in obtaining a good optimizer that can achieve expert-level performance. The authors describe new algorithms that take into account the variable cost (duration) of learning algorithm experiments and that can leverage the presence of multiple cores for parallel experimentation. These proposed algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization for many algorithms including Latent Dirichlet Allocation, Structured SVMs and convolutional neural networks.
 
-* [A Tutorial on Bayesian Optimization](https://arxiv.org/abs/1807.02811) - 2018. [[All Versions](https://scholar.google.com/scholar?cluster=7971934771645047583&hl=en&as_sdt=0,5)]. 
+* [A Tutorial on Bayesian Optimization](https://arxiv.org/abs/1807.02811) - 2018. [[All Versions](https://scholar.google.com/scholar?cluster=7971934771645047583)]. Bayesian optimization is an approach to optimizing objective functions that take a long time (minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of less than 20 dimensions, and tolerates stochastic noise in function evaluations. It builds a surrogate for the objective and quantifies the uncertainty in that surrogate using a Bayesian machine learning technique, Gaussian process regression, and then uses an acquisition function defined from this surrogate to decide where to sample. This tutorial describes how Bayesian optimization works, including Gaussian process regression and three common acquisition functions: expected improvement, entropy search, and knowledge gradient. The authors then discuss more advanced techniques, including running multiple function evaluations in parallel, multi-fidelity and multi-information source optimization, expensive-to-evaluate constraints, random environmental conditions, multi-task Bayesian optimization, and the inclusion of derivative information. The authors conclude with a discussion of Bayesian optimization software and future research directions in the field. This tutorial provides a generalization of expected improvement to noisy evaluations, beyond the noise-free setting where it is more commonly applied. This generalization is justified by a formal decision-theoretic argument, standing in contrast to previous ad hoc modifications. 
 
 
 
diff --git a/docs/awesome/awesome-angular.md b/docs/awesome/awesome-angular.md
@@ -217,7 +217,7 @@ Current Angular version: [![npm version](https://badge.fury.io/js/%40angular%2Fc
 
 ##### Certification
 
-* [Certificates.dev](https://certificates.dev/angular) - **[UNLIMITED ACCESS TO ANGULAR MID-LEVEL CERTIFICATION TRAINING NOW LIVE](https://certificates.dev/angular/free-weekend)** Includes all theory, coding challenges, quizzes, and even a mock exam!
+* [Certificates.dev](https://certificates.dev/angular) - Obtain your Certification of Competence as an Angular Developer.
 * [Angular Academy CA](https://www.angularacademy.ca/angular-certification) - Angular Academy is the #1 provider of hands-on instructor-led classroom training in Canada!
 * [Hackerrank](https://www.hackerrank.com/skills-verification/angular_basic) - Angular (Basic) Skills Certification Test.
 * [Edureka](https://www.edureka.co/angular-training) - Angular Certification Course Online.
@@ -1243,6 +1243,7 @@ to simplify usage and allow quick customization.
 * [Semantic icons](https://github.com/khalilou88/semantic-icons) - A collection of free and open source icons ready for use in your angular projects using the component attribute selector and the svg tag.
 * [coolshapes](https://github.com/ngxpert/coolshapes) -  An Angular library aiming at allowing developers to use cool-looking abstract shapes with little grainy gradients from [coolshapes](https://coolshap.es/).
 * [lucide](https://github.com/lucide-icons/lucide) - An open-source icon library that provides 1000+ vector (svg) files for displaying icons and symbols in digital and non-digital projects. The library aims to make it easier for designers and developers to incorporate icons into their Angular projects by providing an official [package](https://lucide.dev/guide/packages/lucide-angular).
+* [iconic](https://github.com/nginf/iconic) - Angular library to provide components of open-source icon libraries.
 
 #### Images
 
diff --git a/docs/awesome/awesome-github-wiki.md b/docs/awesome/awesome-github-wiki.md
@@ -1,5 +1,5 @@
 <div class="github-widget" data-repo="MyHoneyBadger/awesome-github-wiki"></div>
-## Awesome GitHub Wikis [![Awesome](https://awesome.re/badge.svg)](https://awesome.re) [![Awesome Lint](https://github.com/MyHoneyBadger/awesome-github-wiki/actions/workflows/action.yml/badge.svg?branch=main)](https://github.com/MyHoneyBadger/awesome-github-wiki/actions/workflows/action.yml?query=branch%3Amain) [![Track Awesome List](https://www.trackawesomelist.com/badge.svg)](https://www.trackawesomelist.com/MyHoneyBadger/awesome-github-wiki/)
+## Awesome GitHub Wikis [![Awesome](https://awesome.re/badge.svg)](https://awesome.re) [![Track Awesome List](https://www.trackawesomelist.com/badge.svg)](https://www.trackawesomelist.com/MyHoneyBadger/awesome-github-wiki/)
 > A curated list of awesome GitHub Wikis
 
 Every repository on [GitHub.com](https://github.com/) comes equipped with a section for hosting documentation, called a [Wiki](https://docs.github.com/en/communities/documenting-your-project-with-wikis/about-wikis). Repository's Wiki shares long-form content about project, such as how to use it, how you designed it, or its core principles. A README file quickly tells what project can do, while use a Wiki to provide additional documentation.
diff --git a/docs/awesome/awesome-love2d.md b/docs/awesome/awesome-love2d.md
@@ -30,6 +30,7 @@ A categorized community-driven collection of high-quality, awesome [LÖVE](http:
 * [pathfun](https://codeberg.org/apicici/pathfun) - Pure Lua library for 2D pathfinding using the funnel algorithm.
 * [beehive.lua](https://github.com/drhayes/beehive.lua) - A functional behavior tree implementation.
 * [Luafinding](https://github.com/GlorifiedPig/Luafinding) - Class-based A* implementation written purely in Lua.
+* [LÖVElyTrees](https://github.com/Nrosa01/LOVElyTrees) - Fully featured behaviour tree implementation with tree rendering.
 
 ## Animation
 *Animation & Frame-Managing Libraries*
@@ -171,6 +172,7 @@ A categorized community-driven collection of high-quality, awesome [LÖVE](http:
 * [nvec](https://github.com/MikuAuahDark/NPad93/blob/master/nvec.lua) - Hump.vector-compatible LuaJIT FFI-accelerated 2D vector library.
 * [shash](https://github.com/rxi/shash) - A simple, lightweight spatial hash for Lua.
 * [vector.lua](https://github.com/themousery/vector.lua) - A simple vector library based on the PVector class from processing.
+* [Vornmath](https://github.com/DUznanski/vornmath) - The most comprehensive small vector & matrix, complex number, and quaternion library for Lua.
 
 ## Music
 *Music related libraries*
@@ -378,9 +380,10 @@ A categorized community-driven collection of high-quality, awesome [LÖVE](http:
 * [Notepad++](https://notepad-plus-plus.org) - Notepad++ is a free source code editor and Notepad replacement that supports several languages.
 	* [LÖVE API for Notepad++](https://github.com/dail8859/love-api-npp) - Code completion and documentation for Notepad++.
 * [Visual Studio Code](https://code.visualstudio.com/) - VS Code is a new type of tool that combines the simplicity of a code editor with what developers need for their core edit-build-debug cycle.
-	* [Visual Studio Code LÖVE Launcher](https://marketplace.visualstudio.com/items?itemName=JanW.love-launcher) - A Löve Launcher Extension for Visual Studio Code.
-	* [Lua for Visual Studio Code](https://marketplace.visualstudio.com/items?itemName=trixnz.vscode-lua) - Provides Intellisense and Linting for Lua in VSCode.
 	* [Local Lua Debugger](https://marketplace.visualstudio.com/items?itemName=tomblind.local-lua-debugger-vscode) - Simple Lua debugger with no dependencies. Löve specific launch.json example provided.
+	* [Lua for Visual Studio Code](https://marketplace.visualstudio.com/items?itemName=trixnz.vscode-lua) - Provides Intellisense and Linting for Lua in VSCode.
+	* [Lua Language Server](https://marketplace.visualstudio.com/items?itemName=sumneko.lua) - Various language features for Lua to make development easier and faster; includes LÖVE code completion and documentation.
+	* [Visual Studio Code LÖVE Launcher](https://marketplace.visualstudio.com/items?itemName=JanW.love-launcher) - A Löve Launcher Extension for Visual Studio Code.
 * [Sublime Text](https://www.sublimetext.com) - Sublime Text is a sophisticated text editor for code, markup and prose. You'll love the slick user interface, extraordinary features and amazing performance.
 	* [Package Manager](https://packagecontrol.io/) - The Sublime Text package manager that makes it exceedingly simple to find, install and keep packages up-to-date.
 	* [SublimeLove](https://packagecontrol.io/packages/SublimeLove) - Supports syntax highlighting, auto-completion, and build system.
@@ -401,6 +404,7 @@ A categorized community-driven collection of high-quality, awesome [LÖVE](http:
 * [LÖVE Actions](https://github.com/love-actions) - Build & deploy cross-platform game packages on ***ALL*** popular platforms. Supports Android, iOS, Linux, maxOS, Windows.
 * [love-packager](https://github.com/simplifylabs/love-packager) - Simple CLI to package your LÖVE Game in seconds.
 * [boon](https://github.com/camchenry/boon) - Multi-platform, easy to use tool supporting Windows, macOS, Linux.
+* [LÖVE Game Development & Automated Build System](https://github.com/Oval-Tutu/bootstrap-love2d-project) - Preconfigured VSCode/Codium. Build for Android, iOS, HTML5, Linux, macOS and Windows and automatically publish to Itch.io.
 * [love-export](https://github.com/dmoa/love-export) - Fast and simple command-line tool that builds binaries for you. Supports Windows, macOS, and Linux.
 * [love-release](https://github.com/MisterDA/love-release) - A Lua script that automates game distribution. Supports Windows, macOS, Debian, Linux.
 * [lovesfx](https://github.com/tpimh/lovesfx) - Packs love games in a single file for windows.
diff --git a/docs/awesome/awesome-neovim.md b/docs/awesome/awesome-neovim.md
@@ -338,7 +338,7 @@
 - [2KAbhishek/markit.nvim](https://github.com/2KAbhishek/markit.nvim) - Improved global marks and project wide bookmarks, to quickly navigate files.
 - [you-n-g/navigate-note.nvim](https://github.com/you-n-g/navigate-note.nvim) -  Integrating note-taking capabilities with navigation/marking.
 - [zongben/navimark.nvim](https://github.com/zongben/navimark.nvim) - An easy and powerful bookmark manager with telescope.
-
+- [francescarpi/buffon.nvim](https://github.com/francescarpi/buffon.nvim) - Buffers navigation, reorganize and close.
 <!--lint disable double-link -->
 
 
diff --git a/docs/awesome/awesome-network-analysis.md b/docs/awesome/awesome-network-analysis.md
@@ -498,6 +498,7 @@ __Note:__ searching for ‘@’ will return all Twitter accounts listed on this
 -   [qgis-edge-bundling](https://github.com/ait-energy/qgis-edge-bundling) - Implementation of force-directed edge bundling for the QGIS Processing toolbox.
 -   [Radatools](https://deim.urv.cat/~sergio.gomez/radatools.php) - Set of tools intended for the analysis of complex networks, built on top of [Radalib](http://deim.urv.cat/~sergio.gomez/radalib.php), a library written in Ada.
 -   [Retina](https://ouestware.gitlab.io/retina) - Web application to share GEXF and GraphML network visualizations.
+-   [Scikit-network](https://github.com/sknetwork-team/scikit-network) - Open-source library for machine learning on graphs.
 -   [SageMath](https://www.sagemath.org/) - Free open-source mathematics software with extensive [graph capabilities](http://doc.sagemath.org/html/en/reference/graphs/index.html).
 -   [Segrada](https://www.segrada.org/) - Cross-platform tool to build and visualize semantic graph databases.
 -   [Siena](https://www.stats.ox.ac.uk/~snijders/siena/) - Simulation Investigation for Empirical Network Analysis. Formerly a Windows program, now developed as the RSiena R package.
@@ -998,8 +999,9 @@ Alden S. Klovdahl,
 [Eran Rivlis](https://github.com/erivlis),
 [Rohan Dandage](https://github.com/rraadd88), 
 [Benjamin Smith](https://github.com/benyamindsmith), 
-[Beth Duckles](https://github.com/bduckles) and 
-[Lei Cao](https://github.com/cllei12) - 
+[Beth Duckles](https://github.com/bduckles), 
+[Lei Cao](https://github.com/cllei12) and 
+[Simon Delarue](https://www.simondelarue.com/) - 
 have waived all copyright and related or neighboring rights to this work.
 
 Thanks to [Robert J. Ackland](https://github.com/rjackland), 
diff --git a/docs/awesome/awesome-vlm-architectures.md b/docs/awesome/awesome-vlm-architectures.md
@@ -589,7 +589,7 @@ Haoyu Lu, Wen Liu, Bo Zhang, Bingxuan Wang, Kai Dong, Bo Liu, Jingxiang Sun, Ton
 
 DeepSeek-VL2 is an advanced series of large Mixture-of-Experts (MoE) Vision-Language Models that significantly improves upon its predecessor, DeepSeek-VL, by incorporating a dynamic tiling vision encoding strategy for high-resolution images and leveraging DeepSeekMoE models with Multi-head Latent Attention for efficient inference. It is trained on a large vision-language dataset, shows top performance in tasks.
 
-[![arXiv](https://img.shields.io/badge/arXiv-2204.14198-b31b1b.svg?style=flat-square)](https://arxiv.org/abs/2204.14198)
+[![arXiv](https://img.shields.io/badge/arXiv-2412.10302-b31b1b.svg?style=flat-square)](https://arxiv.org/abs/2412.10302)
 [![GitHub](https://badges.aleen42.com/src/github.svg?sanitize=true)](https://github.com/deepseek-ai/DeepSeek-VL2)
 [![HuggingFace](https://img.shields.io/badge/🤗-Open%20In%20Spaces-blue.svg)](https://huggingface.co/spaces/deepseek-ai/deepseek-vl2-small)  
 Zhiyu Wu, Xiaokang Chen, Zizheng Pan, Xingchao Liu, Wen Liu, Damai Dai, and et al.
diff --git a/docs/awesome/search-engine-optimization.md b/docs/awesome/search-engine-optimization.md
@@ -142,6 +142,7 @@ A helpful checklist / collection of Search Engine Optimization (SEO) tips and te
 - [Webpagetest.org](https://www.webpagetest.org/) - Web Page Test gives you an overall performance waterfall as well as rendering timeline for sites. It also provides critical insight into time to first byte and what could be holding back web page performance.
 - [WooRank](https://www.woorank.com/) - WooRank will help you to address issues on your site & identify opportunities to push you ahead of the competition.
 - [Awesometechstack.com](https://awesometechstack.com/) - AwesomeTechStack provides insights into the security, modernity, and performance of any website's technology stack and guidance to improve web vitals and the technology stack.
+- [OptimalUX](https://optimalux.com/seo-patching) - Optimize your site with seamless SEO patching and an A/B testing tool built on top of Cloudflare for easy integration. 
 
 ### Keywords
 

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`@@ -292,7 +292,7 @@ Contributions are greatly welcomed! Please refer to [Contribution Guidelines](ht`
`292`	`292`
`293`	`293`	* [Practical Bayesian Optimization of Machine Learning Algorithms](https://proceedings.neurips.cc/paper/2012/hash/05311655a15b75fab86956663e1819cd-Abstract.html) - *NeurIPS'12*, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=14442949298925775705)]. The use of machine learning algorithms frequently involves careful tuning of learning parameters and model hyperparameters. Unfortunately, this tuning is often a “black art” requiring expert experience, rules of thumb, or sometimes brute-force search. There is therefore great appeal for automatic approaches that can optimize the performance of any given learning algorithm to the problem at hand. This work considers this problem through the framework of Bayesian optimization, in which a learning algorithm’s generalization performance is modeled as a sample from a Gaussian process (GP). The authors show that certain choices for the nature of the GP, such as the type of kernel and the treatment of its hyperparameters, can play a crucial role in obtaining a good optimizer that can achieve expert-level performance. The authors describe new algorithms that take into account the variable cost (duration) of learning algorithm experiments and that can leverage the presence of multiple cores for parallel experimentation. These proposed algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization for many algorithms including Latent Dirichlet Allocation, Structured SVMs and convolutional neural networks.
`294`	`294`
`295`		`-* [A Tutorial on Bayesian Optimization](https://arxiv.org/abs/1807.02811) - 2018. [[All Versions](https://scholar.google.com/scholar?cluster=7971934771645047583&hl=en&as_sdt=0,5)].`
	`295`	+* [A Tutorial on Bayesian Optimization](https://arxiv.org/abs/1807.02811) - 2018. [[All Versions](https://scholar.google.com/scholar?cluster=7971934771645047583)]. Bayesian optimization is an approach to optimizing objective functions that take a long time (minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of less than 20 dimensions, and tolerates stochastic noise in function evaluations. It builds a surrogate for the objective and quantifies the uncertainty in that surrogate using a Bayesian machine learning technique, Gaussian process regression, and then uses an acquisition function defined from this surrogate to decide where to sample. This tutorial describes how Bayesian optimization works, including Gaussian process regression and three common acquisition functions: expected improvement, entropy search, and knowledge gradient. The authors then discuss more advanced techniques, including running multiple function evaluations in parallel, multi-fidelity and multi-information source optimization, expensive-to-evaluate constraints, random environmental conditions, multi-task Bayesian optimization, and the inclusion of derivative information. The authors conclude with a discussion of Bayesian optimization software and future research directions in the field. This tutorial provides a generalization of expected improvement to noisy evaluations, beyond the noise-free setting where it is more commonly applied. This generalization is justified by a formal decision-theoretic argument, standing in contrast to previous ad hoc modifications.
`296`	`296`
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