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This project refactors Graphite to create a more generalized graphic representation of paints. It introduces dedicated Gradient and Pattern nodes, as well as updated Fill and Stroke nodes that can consume generalized graphic sources, not just the current fill and color types. The project also enables the standalone rendering of colors, gradients, patterns, and other compatible graphics as ordinary layers. The implementation will update Graphite's graphics, nodes, and Vello/SVG renderers to support gradient strokes and pattern fills. Additionally, SVG import will be supported for the MVP subset of these features. Stretch goals include broader gradient support, such as focal points, conic gradients, and gradients along strokes.

<p>The aim of this project is to build a web portal and incorporate it into the existing EOS ecosystem. This web portal will allow users to request for new icons and features into EOS.</p>

gprMax users currently visualize simulation output using static terminal scripts with no interactivity. This project builds a reactive web-based dashboard using marimo, replacing that workflow with four components: parameter controls with live geometry preview, a simulation progress tracker, post-processing visualization for A-scans and B-scans, and a set of Reactive Recipes for common modelling scenarios. A working prototype covering all three visualization components is already built and running against real gprMax HDF5 output on the devel branch.

<p>DaRu (Data Analysis in RUby) is a library for storage, analysis, manipulation and visualisation of data. Currently it is doing good job for scientific usage(in IRuby notebook), but if user want to visualize some data for integration into existing web application, then Daru methods have to output result/chart/graph/datatable such a way that output is directly usable into web application (in html file). Also Daru must have better import/export technique so that new importers/exporters can be defined easily. Mostly modern web application will use NoSql database, that is easy to scale. It will be very useful if Daru can connect with relational or non-relational database and make query operations easy. This project aims to make Daru more powerful for web application to use it as data analysis and visualisation tool, by working on above issues.</p>

<p>Scrapy currently uses Python's inbuilt RobotFileParser which is not fully compliant, but the more compliant alternatives are difficult to package and use within Scrapy’s pure-python development tree. This project is about introducing a new interface for robots.txt parsers in scrapy, allowing users of scrapy to substitute a different robots.txt parser. The stretch goal of the project is to create a pure python parser for robots.txt files.</p>

cve-bin-tool currently has explicit, pre-defined mappings between binary signatures and a list of CPE identifiers for that product. Which can sometimes result in false positives. This project is intended to improve our product mapping and reduce false positives.

<p>D is a modern language that offers the convenience of Python at the speed of C++. While D is constantly evolving, its support for scientific research is still sparse. This project will be focused on improving Mir (Phobos’s upcoming science module) by adding a universal non-uniform random number generator (RNG) based on Tinflex. In contrast to the standard acceptance-rejection method, Tinflex requires only knowledge of the probability density function, its first two derivatives and a partitioning in intervals with at most one inflection point and is thus easier to use. Moreover a lot of common non-uniform random distributions will be added.</p>

This project aims to implement some new language features introduced in Fortran 2018 and Fortran 202x, enhancing gfortran's compatibility with newer Fortran standards.

This proposal aims to develop an information theoretic approach to filter out artificial information from real information in geospatial datasets. The project will use the bitinformation framework to distinguish between real and false information, where real information is defined as the mutual information between adjacent bits. The proposed algorithm will filter out artificial information, which is a consequence of prior compression, from real information, which reflects the underlying signal. The project will involve a theoretical review, test case generation, algorithm development, evaluation, and integration into xbitinfo, a software package that implements the bitinformation framework. The resulting artificial information filter will provide a valuable tool for users who may not have access to high-precision/uncompressed data, and it will enhance the accuracy and reliability of geospatial data analysis. As a participant in the Google Summer of Code, I am excited about the opportunity to work on this project and contribute to the development of an open-source software tool that will benefit the scientific community.

<p>StarFix is a cross-platform client-side application that would let you open a file in the Editor of your choice (vscode, eclipse, intellij, emacs, vi, etc.) and other commands locally directly from the browser or file system. This will enable an option to “Open in IDE” through browser extension on various websites like Github, Gitlab, etc similar to the one you see in this demo below. This season we aim to improve the existing tool by resolving the existing issues and adding new features that the user may need .</p>

This project will primarily change the command system for TechDraw from a pre-selection to a post-selection. So that the user can click on a command and then the feature they want to annotate. Additionally visual guidance will be added for these commands so it is natural the new post-selection. The tools that cannot be changed to post-selection will instead be grayed-out if the user has not picked a correct feature. Also the toolbars will be changed.

<p>Magneto- and Electro-Encephalography (M/EEG) are neuroimaging techniques that non-invasively record human brain activity typically using ~ 100-300 simultaneous sensors sampled at 1kHz. Because these sensors are positioned outside subjects' head, they capture a complex mixture of neural sources, and can therefore be particularly challenging to analyze.</p> <p>Machine learning algorithms recently proved to be extremely efficient at decoding these high-dimensional signals - that is: at inferring the neural and cognitive mechanisms and dynamics underlying the M/EEG recordings.</p> <p>The aim of the present project is to interface the signal processing functions already implemented in the MNE-Python MEG analysis library with the scikit-learn machine-learning library. Specifically, we will implement a series of transformers and a dedicated pipeline with the same Application Program Interface (API) as scikit-learn.</p>

A library management add-on for managing Different online-offline FreeCAD components is a utility application for FreeCAD which allows an easy and structured management of components/models available online or offline and to easily be inserted into the FreeCAD document. Its basic functionalities is to facilitate individual component from the online repository to be able to browsed, downloaded, and extended by already available local components. For a Library management system, a robust repository of data and an local storage structure is must. This is important as user need to brows through the all the existing models which are the part of the repository and download only selective models. A structured local storage will not only also allow users to easily add their own models to the local library management system, but also to the online repository open to public. For maintaining the metadata of these, a well defined storage structure is required. All this need to have an proper interface to be operable. The interface for the above will be developed as both a Graphical Interface and module for python scripting.

The project aims to migrate the FreeCAD documentation system to a file-based, versioned system based on Git and Markdown so that it supports multiple languages for users globally and allows switching between offline and online versions. The current system is at constant risk of losing data due to the MediaWiki software used to support it. The project involves community interaction to research solutions and then upgrade the entire system to new software. The completion of the project will result in a better documentation system for the FreeCAD software that is robust and supports version control. It will also allow the user to download, or pack, the entire FreeCAD documentation to be able to access it on a machine with no internet connection, which is the goal of switching from MediaWiki to a file-based solution.

<p>Implementing precise collection as a first step towards a better garbage collector.</p>

<p>When astronauts travels to Mars, or even other planets, they will be exposed to a number of hazards e.g. radiation, microbes in the spacecraft, planetary surface toxic dust. This project mainly revolves around configuring biometric signal sensors.</p> <p>The project has an initial phase of surveying the commercial sensors available, and selecting one, or many, after extensive research. The sensors are selected such that they read the biometrics like Heart Rate, Accelerometer data, Respiration rate, body temperature, Pulsioximetry data,Respiratory Volume, ECG, etc.</p> <p>The second phase involves developing the Device Server itself in Tango, for these selected sensors. This project is part of the current studies on the simulation of Astronaut’s Health Monitor Systems.</p>

<p>Direct Rendering Manager subsystem has pretty elaborate ioctl interface, and it might be useful to be able to support its decoding.</p>

<p>The Activeloop organization provides data scientists worldwide with a solution to these problems so that they can spend more time training their models and saving resources. Hub is a package that stores petabyte-scale datasets as a single numpy-like array on the cloud, so one can seamlessly access and work with it from any machine. Hub has a simple API that enables its users to obtain a lot of functionality with just a few lines of code. However, as a complete beginner, it is difficult for many to learn the nuances of a new package. Reading the documentation is something that helps, but it is not exactly a fun way of learning something new. It can become tedious and boring. This is exactly why it would be great if Hub had a package that interactively teaches the basics of hub in the CLI itself.</p>

This project aims to enhance the testing of the Wild linker by leveraging existing linker test suites, providing strong support for its development. Develop some programs that apply existing linker test suites to Wild, automatically categorizes the results to some extent, and visualizes them in a format that allows Wild developers to easily review them. Subsequent work will focus on a variety of improvements to Wild, including adding support for new architectures, fixing existing bugs, implementing new features, and more.

My proposed project aims to enhance the Borg ecosystem by tackling three main areas: Filter option in diff and extract view in Vorta, implement live binary tests in Vorta, and bootstrap a Borgmatic restore from nothing. These improvements will help users more efficiently and effectively manage their backups, improve testing processes, and facilitate disaster recovery. Firstly, I will implement a search function that allows users to filter files by search string in Vorta's diff and extract view. I will also add the "Change" filter to the comparison view, which will filter only removed, added, and modified files. This task also includes adding syntax to allow advanced filtering and filter by size range as a stretch goal. These enhancements will help users easily find and manage their backup files. Secondly, I will improve BorgBackup testing by implementing live binary tests in Vorta. Currently Vorta contains stdout and stderr mock files for the Borg JSON output of various commands for testing purposes but there’s no actual running of the commands. Moreover, it only supports one mock file while Vorta supports multiple Borg versions where output syntax may vary. This project aims to improve Vorta Testing by implementing live binary tests that run on multiple actual Borg versions/binaries. I'll also integrate live binary tests with Vorta's CI/CD pipeline. Finally, I will implement bootstrap functionality in Borgmatic to restore backups from nothing, enabling users to easily recover their data in the event of a disaster. I will implement this feature by providing clear and comprehensive documentation, which will outline the steps required to perform a Borgmatic restore from scratch. This will significantly improve disaster recovery capabilities and reduce the risk of data loss.

This proposal addresses the lack of debugging tools for pocketpy, a lightweight Python interpreter for game scripting. It will develop a VSCode debugging extension along with Debug Adapter Protocol (DAP) support. The solution includes a debugger core that integrates with pocketpy's interfaces to support breakpoints, variable inspection, and call stack visualization, paired with a VSCode extension providing an intuitive debugging interface. Deliverables include a DAP-compatible debugger core, a VSCode extension with debugging configuration capabilities, and APIs for runtime inspection of variables and stack frames.

<p>Adding support for archive storage (i.e in the form of .zip or .tar.* family) of models for all pre-existing models in DFFML and update tests, documentation and fix any model specific bugs that come along the way while implementing this feature. This will not only save the model state but also all the configuration of a model. There would be two benefits of this implementation to the user:</p> <ol> <li>Increased Reproducibility of DFFML models. </li> <li>Better Portability of DFFML models.</li> </ol>

Description:Kruize Autotune is a Performance Tuning Tool for Kubernetes. . It uses Hyper Parameter Optimization to tune the language runtime layers of a given application. System component metrics can give a better look into what is happening inside them. Metrics are particularly useful for building dashboards and alerts. Kubernetes components emit metrics in Prometheus format. This format is structured plain text, designed so that people and machines can both read it.Following are four common methods of hyperparameter optimization for machine learning in order of increasing efficiency: 1.Manual 2.Grid search 3.Random search 4.Bayesian model-based optimization The aim of Bayesian reasoning is to become “less wrong” with more data which these approaches do by continually updating the surrogate probability model after each evaluation of the objective function. At a high-level, Bayesian optimization methods are efficient because they choose the next hyperparameters in an informed manner. The basic idea is: spend a little more time selecting the next hyperparameters in order to make fewer calls to the objective function. In practice, the time spent selecting the next hyperparameters is inconsequential compared to the time spent in the objective function. By evaluating hyperparameters that appear more promising from past results, Bayesian methods can find better model settings than random search in fewer iterations.So,this project aims at tuning kubernetes applications using bayesian optimization.