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Cytoscape.js is an open-source JavaScript-based graph theory library that can be used for graph analysis and visualisation. It contains many useful functions in graph theory and can be used headlessly on Node.js to do graph analysis in the terminal or on a web server. Cytoscape.js supports several types of graphs, including traditional graphs, directed graphs, undirected graphs, multigraphs and hypergraphs (with compound nodes, but not yet with hyperedges). It was designed to make it as easy as possible for programmers and scientists to use graph theory in their apps. The application receives constant updates from open-source contributors, which are bundled in a patch for release. The current system of this release is primarily manual. This proposal aims toward developing GitHub action that automates the task of release resulting in lower costs of making the release.

This project introduces a network abstraction layer to libkboot, enabling boot artifacts to be fetched from remote sources without disrupting the existing loading flow. On top of this abstraction, it implements a minimal, bootloader-focused HTTP/1.1 client and HTTPS support via a TLS-backed transport. This allows kboot to support network-based boot workflows through a clean, maintainable, and extensible interface for future network features.

<p>MapMint4ME (MM4ME) is an android application with the capability to record data on the field without internet connectivity. It gives users the facility to store any kind of alphanumeric data. Users are able to take photos, record their position, and view their current location on a map based on the configuration settings of their MapMint server. When the user returns back to a place with access to the internet, the recorded data can be uploaded back to the MapMint server. The aim of the project is to extend Augmented Reality (AR) support in the MapMint4ME application. The motivation for the project was to improve field data acquisition. AR Simulation module provides users capability to visualize 3D objects simulation in real world scenes. AR Area module allows users to export measured "AR measurements" into text format and which can be used for further analysis. MapMint4ME works without the internet too, so adding a GPS Satellite information module which gives detailed analysis about GPS Coordinates and GPS Satellite information improves the information gathering process and makes it more efficient.</p>

<p>In many fields of science and engineering (such as electrodynamics, acoustics, and quantum mechanics), the partial differential equations (PDEs) can be complex-valued. Currently, the finite element solver of the FEniCS Project, DOLFIN, supports only real floating-point arithmetic limiting the scope of application on these fields. The primary goal of this project is to extend the capabilities of DOLFIN to support complex numbers, and thus to directly represent complex-valued fields, opening up the possibility of the solution of large-scale complex-valued PDEs using FEniCS.</p>

<p>While observing a satellite from the ground station, it could lead to more than one satellite being visible from the ground station at a given time due to the increase in the number of the deployed satellites. These satellites may transmit with the same or near frequencies to the ground station which often leads to interference in the observed results.</p> <p>The basic aim of the project is to develop a Python module that enables the users to determine when and over which area the interference occurs. This module would be used by SatNOGS as an internal or an external tool to let the observers know which other satellites are expected to be found in the results of their observations</p>

Generics are a common functionality found in statically typed programming languages, allowing easier maintenance of programs which differ only in their types. However, generics are not yet supported in the statically-typed LPython. Generics in LPython can be implemented similar to the approach taken by C++ templates. We can regard generic functions as functions with type parameters, whose parameters are made concrete and checked by function calls on AST level. The end result of the project would be generic function support for LPython with option for specialization, along with sufficient integration tests and documentations.

This project aims to enhance OpenMP support in LFortran by developing a unified ASR node, that encapsulates all core and advanced OpenMP directives and clauses. The new design will enable robust handling of constructs like Parallel, Do, Teams, Tasks, Distribute, and Target offloading, ensuring systematic lowering to runtime calls. Key deliverables includes modifying the current design of representing OpenMp directives in ASR and implementing it's features via runtime calls and explore Target Offloading support. This will make LFortran a more powerful and extensible tool for HPC applications.

This project aims to add support for Multi-Channel Imaging in caMicroscope, a platform used for visualizing and analyzing medical imaging data. Currently, the platform only supports datasets with a spatial representation for up to three channels. This project aims to create a function that can take in numerical data in CSV format with an arbitrary number of channels and select three fields to assign to red, green, and blue channels to generate an image. This project will allow Researchers and Deep Learning partitioners to make sense of higher-dimensional data and improve their analysis of medical imaging data.

DocArray is a library for representing, sending, and storing multi-modal data, with a focus on applications in ML and Neural Search. It currently supports several deep learning frameworks, including PyTorch and TensorFlow. Jax is becoming increasingly popular for deep learning, so we want to integrate it into DocArray. The project we propose is to add Jax as a backend for DocArray, alongside PyTorch and TensorFlow. The first part would involve rewriting and translating all of the computational backend functions of DocArray with the Jax framework. Then, we would battle-test the implementation against a real Jax use case, such as integrating DocArray with Jax support for model training and serving.

This project adds Hyperlight as a new VMM backend in KraftKit, enabling Unikraft unikernels to run with `kraft run --plat hyperlight`. The work will introduce platform registration and a new Hyperlight driver in the machine layer, then implement the full machine lifecycle: create, start, stop, delete, inspect, list, logs, and watch. It will start with a minimal boot path for a basic kernel, then extend to networking and storage support where possible. By the end of the project, KraftKit will have first-class Hyperlight support, and users will be able to manage Hyperlight machines through the standard CLI workflow.

<h3>Module for Approximate Bayesian Computation</h3> <p>Approximate Bayesian Computation (ABC) algorithms, also called likelihood free inference techniques, are a family of methods that can render virtually impossible models to feasible scale. Additionally, the ABC approach has proven to be successful over likelihood based methods in several instances and is conceptually linked to recent and very innovative Deep Learning developments such as Generative Adversary Nets. We propose to implement a module for ABC in PyMC3, specifically Sequential Monte Carlo-ABC (SMC-ABC). Our work will signify a meaningful increase in the spectrum of models that PyMC3 will be able to perform.</p>

As a tool aiming to deliver database assets for non-technical personnel, Mathesar should support more data types for the purpose of straightforward operation and easy understanding to meet the different requirements of users. Currently, the data types that Mathesar can support are limited and do not include JSON. Allowing users to read and manipulate data in JSON format can significantly improve the practical value of Mathesar and meet various application scenarios. In this project, we will suport importing, adding, change a database field to a JSON object and enable data validation, modification, and browsing. Also, it will allow the grouping, filtering, and sorting operations based on JSON keys.

<p>In this project, 5 new general solvers that can solve specific cases of system of ODEs and two techniques, namely, division of system of ODEs into subsequent subsystems and reduction of higher order ODEs to first order ODEs are added to the ODE module of SymPy to greatly enhance SymPy's capabilities to solve system of ODEs. These capabilities not only make the ODE module compact, by eliminating a significant number of special case solvers which mostly handle 2 to 3 equations per system, but it also manages to expand SymPy's current capabilities as there are many cases of systems of ODEs that the library can't handle right now, which will be possible after these solvers and functionalities are added.</p>

<p>This proposal focuses on developing a prototype in Electron.js of the SEAGrid native application, currently implemented in JavaFX. Once prototyped, translation from Django based web applications to native Electron applications can be generalized to an Electron based Airvata Framework. An Electron based Airvata Framework will allow for quick translation from Django based web-apps for specific Science Gateway applications and provide a more stable platform with larger community support.</p>

<p>QEMU's TCG just-in-time compiler translates target CPU instructions into host CPU instructions so that programs written for other CPU architectures can be run on any host. Modern CPUs feature vector processing instructions, sometimes called Single Instruction Multiple Data (SIMD) instructions, which perform the same operation on multiple data elements at once. Intel's SSE and AVX instruction set extensions were introduced for x86 CPUs for this purpose.</p> <p>The target/i386 front-end has support for TCG emulation of SSE4.2, but does not feature support for later vector extensions, such as AVX. The goal of the proposed project is to implement and test AVX instructions that are currently not implemented in QEMU's TCG.</p>

Currently, the Newt editor supports Systems Biological Graphical Notation (SBGN) and Simple Interactive Format (SIF) formats while also allowing importing/exporting data in Systems Biology Markup Language (SBML) format. Conversion to SBML format from SBGN or SIF is lossy since the data models are not fully compatible. Information may be lost during conversion to SBML format. This is why supporting the SBML format in Newt is crucial. The main feature proposed to be implemented is for users to create SBML models from scratch in Newt without the need to convert them to other data formats. An additional palette will be added with the corresponding notation to the existing palette for the user to create new SBML models or edit an existing one by dragging elements from the palette.

After WasmEdge provides an experimental API, WASI Socket, for supporting Berkeley Sockets API in Wasm. WasmEdge enabled a new way to open a new socket, listen to an existed socket, and send and receive data. Moreover, it will be nice if we can do more things in the related features such as SSL support. To achieve this feature, one possible way is to compile the OpenSSL library to Wasm and link it as a library. However, the performance may be not good, because all the computation jobs are done at the wasm level. Here is an alternative way, instead of the previous one, we can wrap the OpenSSL library to Wasm external functions. For example, binding `ssl_connect` to `(import "openssl" "ssl_connect" ... )`. Unfortunately, this is not an easy way to do it. To simply the workload, we decide to implement the WASI-crypto proposal first, and then use this proposal to make the above things happen.

<p>LLVM offers 2 useful YAML tools, yaml2obj and obj2yaml. The former one reads YAML files and emits object files, e.g., ELF, COFF and MachO. The latter one does the reverse, reads object files and emits YAML files. We use these tools to write unit tests for binary tools, e.g., llvm-objdump, llvm-nm, llvm-readelf, as YAML is easier to read and edit than raw assembly codes and pre-built binaries. More importantly, YAML keeps our tests code base maintainable. However, yaml2obj currently doesn’t support generating DWARF sections very well (we have to hardcode the Content field of DWARF sections and it is not intuitive). This project aims to add DWARF support for yaml2obj (ELF part), which will ease our pain crafting DWARF tests.</p>

<p><strong>Research Information Retrieval for Federated Learning</strong></p><p>The deadline to create an initial application has passed for the May 2020 Outreachy internship cohort. We are no longer accepting initial applications for internships. We encourage you to sign up for the announcements mailing list to get an email when the next round opens. Why apply to Outreachy? &gt; Start your initial application &gt; May 2020 Outreachy internship cohort &gt; Mozilla Community details are hidden until you are approved to participate as a mentor or coordinator.</p><p><br></p><p><strong>Mentorship Cohort:</strong> 2020</p>

<p><strong>Offline Support Pages for Certificate Errors</strong></p><p>The deadline to create an initial application has passed for the May 2020 Outreachy internship cohort. We are no longer accepting initial applications for internships. We encourage you to sign up for the announcements mailing list to get an email when the next round opens. Why apply to Outreachy? &gt; Start your initial application &gt; May 2020 Outreachy internship cohort &gt; Mozilla Community details are hidden until you are approved to participate as a mentor or coordinator.</p><p><br></p><p><strong>Mentorship Cohort:</strong> 2020</p>

<p><strong>Help Tor Project support our users</strong></p><p>The deadline to create an initial application has passed for the May 2021 Outreachy internship cohort. We are no longer accepting initial applications for internships. We encourage you to sign up for the announcements mailing list to get an email when the next round opens. Why apply to Outreachy? &gt; Start your initial application &gt; May 2021 Outreachy internship cohort &gt; Tor Project Community details are hidden until you are approved to participate as a mentor or coordinator.</p><p><br></p><p><strong>Mentorship Cohort:</strong> 2021</p>

<p>Lua is a powerful, efficient, lightweight and dynamically typed scripting language supporting a variety of different programming paradigms. Typed Lua extends Lua with an optional type system allowing the use of both static and dynamic typing in the same code base, while preserving existing Lua run-time semantics. It achieves that by introducing optional type annotations and applying local type inference. This richer type system not only enforces stronger program behavior guarantees, but also facilitates the development of more powerful developer tools.</p> <p>Programmers nowadays expect that their editors/IDEs suggest accurate completion candidates, perform asynchronous - as you type - error checking and support type queries such as "go to definition" or "find all references".</p> <p>The main goal of the project is to add such features for Typed Lua in a wide range of popular editors. Furthermore, it shall be investigated how a modern, asynchronous editor plugin architecture could be designed.</p>

<p>Pywikibot is a popular framework for writing MediaWiki bots. Thanks is an extension for MediaWiki wikis which is designed to allow users to send quick "thank you" messages to other users for edits or Flow comments. Pywikibot does not currently have support for Thanks, meaning that bots using Pywikibot cannot send thanks, use the log of thanks on a wiki, or compile statistics on thanks.</p> <p>This project proposes to implement Pywikibot support for Thanks by:</p> <ul> <li>Implementing functionality to send thanks for revisions and Flow posts.</li> <li>Using existing classes to do useful things with metadata related to thanking.</li> <li>Creating a script to compile statistics of frequent thankers on a particular wiki.</li> <li>Modifying the Thanks extension code where necessary to achieve proper integration.</li> </ul> <p>Also included in the proposal is at least one to-be-determined contribution to the PHP extension code not directly related to the new Pywikibot functionality.</p>