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Effective connectivity analyses are a staple of many neurophysiological research projects, providing key insights into the healthy functioning of the brain and pathological alterations with disease. MNE-Connectivity leverages the API of the highly-popular MNE-Python signal processing package to provide tools for computing such connectivity. However, given the complexities associated with studying interactions in the brain, it is critical that the metrics for estimating connectivity are robust, and the estimates themselves statistically significant. Naturally, it is essential that such tools are available in open-source toolboxes, something we will address in this project. First, building on recent work in the MNE-Connectivity toolbox, we will implement further support for advanced multivariate connectivity metrics in the form of a dedicated decoding module. This module will provide flexible tools for the data-driven analysis of connectivity that is critical for robust characterisations of network activity in multi-channel recordings. The only comparable implementations of such methods are in toolboxes for the proprietary language MATLAB, limiting the availability of these tools to the wider research community. Second, we will expand the existing set of statistical features available in MNE-Python to include tools for assessing the statistical significance of estimated connectivity, crucial for distinguishing genuine interactions from background noise. In all cases, new features will be complemented with detailed examples and tutorials in the MNE-Connectivity documentation. Altogether, the project will expand the repertoire and quality of open-source tools available for studying connectivity in the brain, a cornerstone of modern neurophysiological research.

This project introduces a library of 7 data structures to the Pharo ecosystem, replacing O(N) bottlenecks with resilient, mathematically proven structures (including B Trees, K-D Trees, and Persistent Arrays). Following a full design approach, the implementations will be stress-tested using Competitive Programming datasets to guarantee architectural integrity under heavy load. Finally, the project utilizes VM-level tools (TimeProfiler and MemoryProfiler) to empirically benchmark performance, delivering production-ready infrastructure alongside interactive visual inspectors.

<p>The main goal of the project is, which the title says, to port Terra to Android devices. Once I already have Terra ported to android, my intention is to build an interface between Terra and the android API, that way I am sure Terra will support a lot of features common smartphones have as well as be able to use this features in different devices that have Android on it. This way, by the end of the project, we will not only have ported Terra to android but also develop an interface in which developers can easily access hardware sensors and functionalities to build their own applications.</p>

<p>DFFML provides APIs for dataset generation and storage, and model definition using any machine learning framework, from high level down to low level use is supported. As the goal of DFFML is to build a community driven library of plugins for dataset generation and model definition, so that developers and researchers easily plug and play various pieces of data with various model implementations or generate datasets using the implemented features to increase the accuracy of output. For this, DFFML needs to implement large number of machine learning models as well as various features. I have planned to add the below listed Models/Algorithms to DFFML.</p> <ol> <li>Model 1: Ordinary Least Square Regression (OLSR)</li> <li>Model 2: Logistic Regression</li> <li>Model 3: k-Nearest Neighbour (kNN)</li> <li>Model 4: Naive Bayes</li> </ol>

The project focuses on developing an Apple Metal port of the Finite-Difference Time-Domain (FDTD) solver machine for gprMax. The current version of gprMax with the implementation of CUDA and OpenCL have shown significant speed ups, but many gprMax users are using Apple Hardware and there could be performance benefits with the availability of a Metal port. Therefore, the goal of the project is first and foremost porting gprMax to Metal in a templating approach similar to CUDA/OpenCL while trying to keep as many kind of common parts from a single code base and template out the differences. Other objectives include creating unit tests for the code base, update documentation on the usability, and run performance benchmark to analyze the benefits

<p>NMatrix is being re-implemented by SciRuby contributors here at <a href="https://github.com/prasunanand/nmatrix_reloaded" target="_blank">https://github.com/prasunanand/nmatrix_reloaded</a>. This re-implementation is having a fast core written using C-API with Ruby front-end. With this project, we aim to create a faster NMatrix which will replace the original NMatrix later on and the source would be simpler and easier to read and improve. This proposal is to add more features to this newer NMatrix such as adding support for LAPACK and BLAS routines, sparse matrix operations, indexing and broadcasting, matrix decomposition etc.</p>

<p>Vert.x has already the capability to send metrics to Hawkular. However, students in thie proposal have to implement the functionality needed so that resources in vert.x can report themselves into the inventory of Hawkular, so that the Hawkular UI and other clients can find them with additional traits like operations, names of metrics etc. This also implies being able to run operations from Hawkular inside vert.x.</p>

<h3>Improving the VM Profiler</h3> <p>The current VM profiler is a sampling profiler cadenced at 1.3GHz tracking down where the time is spent in the C code of the VM (for the interpreter and the GC) and in the machine code zone (for the code generated by the JIT). The VM profiler cannot track down precisely where the time is spent when executing the code generated by the JIT. It can track down in which methods the time is spent, but it cannot track down in which part of those methods the time is spent.</p> <h3>Goals</h3> <ul> <li><strong>Port the VM Profiler to Pharo</strong> (it only works in Squeak for the moment) </li> </ul> <p>By making sure the UI stays independent and by patching all the uses of the library OSProcess to make it work in both Squeak and Pharo</p> <ul> <li><strong>Get a more detailed profiling</strong> </li> </ul> <p>By using the JIT API to map machine code instruction pointer to bytecode program counter, to be able in the end to show in which bytecode range of methods the time is spent instead of only in which methods the time is spent</p>

<p>Currently, EOS delivers icons via a set of well-documented methods, which chiefly include EOS-Icons Package, CDN, or direct download of icons in SVG/PNG formats. This project will be based on developing an npm package that can deliver EOS Icons to its users as an independent component library and create a unified central system that will enable EOS to deliver its icons to various frameworks (e.g. React, Vue and Angular) independently. This npm package will be as lightweight as possible in order to reduce the load put on the browser (using the tree shaking approach), which will result in providing a much smoother workflow to the users. Creating a package of EOS Icons for React, Vue, and Angular frameworks will go a long way in having a more consistent user experience as well as boost capability to accommodate users who migrate from one framework to another.</p>

<p>The main objective of the project is to build a consistent API leveraging MNE-Python to perform connectivity analysis of neural time-series data, such as MEG, EEG and iEEG. The result would be “mne-connectivity” a Python3.6+ software package that allows scientists to explore connectivity based analysis using published algorithms with few barriers because it leverages the widely used open-source platform of MNE-Python.</p>

Pwndbg delivers a powerful debugging experience on traditional Linux systems, but its support for Android environments remains limited. This is largely due to fundamental differences between GNU libc and Android’s Bionic, as well as the lack of tooling for Android-specific components such as Binder. As a result, many existing features either do not work as expected or provide limited insight when analyzing Android binaries. This project aims to address these limitations by introducing structured and practical support for Android within Pwndbg. It will extend existing features to correctly handle Bionic internals, particularly in memory inspection and heap analysis, and improve Binder support by enabling meaningful userspace inspection. Additionally, the project will refine internal abstractions to ensure consistent behavior across LLDB and GDB.

<p>This project aims to add support of Go-Style Channels to the Chapel language. In Go, a channel is used to synchronize and communicate between different goroutines. A similar library can be added to chapel to support efficient communication between various chapel tasks in a lock-free manner. The channels would enable a user to decompose algorithms into tasks in a more logical way. The work will involve studying the performance against the original Go channels.</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>A Linear Algebra Library and a plotting Library are the basic requirements for scientific software to be built on. With this project, we aim to create a language-independent plotting library inspired by Matplotlib, that can be easily bridged to any language</p>

Historically, rustup has relied on $RUSTUP_HOME (defaulting to $HOME/.rustup) and $CARGO_HOME (defaulting to $HOME/.cargo) as monolithic directories for configuration, state, and data. In coordination with the Cargo team, this proposal aims to migrate `rustup` to use the standardized XDG Base Directory Specification on Linux. To achieve this safely, we will transition from a "dual-directory" approach to a "fine-grained" directory approach, unblock Cargo's own XDG migration, and establish a strict backward-compatibility protocol for existing installations.

<p>Provide a new mason package implementing various parallel safe sequential data structures.</p>

<p>PULPino is an open-source microcontroller system, based on a small 32-bit RISC-V core.</p> <p>Importing Arduino Libraries to PULPino architecture should introduce both PULP and RISC-V to the massive Arduino community and would benefit in educational purposes.</p> <p>At the end of this project; all Arduino core libraries would be ported and popular Arduino examples should be tested on PULPino core on FPGA to verify the functionality of the libraries, and all work should be published open source for modification by the open source community.</p>

<p>Enable users to attach a script or Lua function to a watchpoint and execute it when the program flow hits it in LLDB and improve the whole documentation for Lua scripting in LLDB.</p>

<p>Smart HTTP/2-based protocol for Infinispan.</p> <p>The Infinispan Server distribution contains a server module that implements Infinispan’s custom binary protocol called Hot Rod. The protocol was designed to enable faster client/server interactions compared to other existing text based protocols and to allow clients to make more intelligent decisions with regards to load balancing, failover and even data location operations.</p> <p>In 2015 the 2nd version of HTTP protocol (simply HTTP/2) was finally introduced. HTTP/2 provides a lot of features that are also implemented in Hot Rod protocol. For example, using the HTTP/2 will be more preferable than Hot Rod because the size of the requests and responses will be smaller.</p> <p>Expected results of this project are:</p> <ol> <li>Modify current REST Server endpoint for Infinispan to handle HTTP/2 protocol. Modification will be based on implementing features from Hot Rod protocol to HTTP/2.</li> <li>Create HTTP client on Java.</li> </ol>

This proposal aims to improve the performance of the Finite-Difference Time-Domain (FDTD) algorithm within the gprMax software by integrating XPU technology. This enhancement will focus on optimizing cache and memory efficiency, employing non-linear multistep update sequences for better data locality. The deliverables include a new XPUSolver class, optimized field update kernels, a robust testing framework, and comprehensive documentation.

<p>Beamforming is among the most widely used source localization techniques for MEG and EEG data in neuroscience. This project aims at porting an updated version of the five-dimensional beamformer to analyze time-frequency data in source space (Dalal et al., 2008; implemented in MNE-Python as “tf_lcmv”). The update adopts the Hilbert transform to obtain a continuous amplitude and phase time series per frequency band, in contrast to the previous method that utilizes discrete time windows and does not retain phase information. It also adds a weight normalization option to the LCMV beamformer (neural activity index and unit noise gain), which will be available for other LCMV versions as well. Furthermore, statistical testing of the Hilbert beamformer output using the Wilcoxon rank sum test will be implemented to complete the new Hilbert beamformer pipeline. Due to the size of the MEG data and intensiveness of the calculations, the code will be developed with memory efficiency and parallelization in mind. Interactive visualization of source-localized time frequency representations will complete the Hilbert beamformer routine.</p>

**Pwndbg** is a debugger extension that enhances the debugging experience. It currently supports only GDB and LLDB on Linux and macOS. This project introduces Windows support for Pwndbg. Initially, only WinDbg will be supported, but a generic interface could enable support for other debuggers (GDB, LLDB, x64dbg, etc.). The goal is to bring Pwndbg enhancements to the WinDbg console — providing a similar experience to using it with GDB or LLDB. End users should be able to access all native WinDbg features while also using Pwndbg commands and context windows. The project leverages WinDbg’s `wdbgexts` or `DbgEng` to interface with Pwndbg. This requires either an RPC client-server protocol or native integration of Pwndbg on Windows. Pwndbg needs to hook into WinDbg states (registers, memory, etc.) and respond to all command calls from WinDbg.

The major objectives of the project are: 1. Refactoring the Python code to increase efficiency and readability and increase the overall code quality of the entire codebase of the software. 2. To enhance user engagement and support by creating a comprehensive FAQ dataset which is used to train a custom Chatbot. 3. Investigate and discuss different possibilities for multi-GPU model execution, i.e. allow a model to execute (and share memory) across multiple GPUs.