Search Results for "joint"
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Showing 80 open source projects for "joint"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Spring for GraphQL
Spring Integration for GraphQL
Spring for GraphQL provides support for Spring applications built on GraphQL Java. It is a joint collaboration between the GraphQL Java team and Spring engineering. Our shared philosophy is to provide as little opinion as we can while focusing on comprehensive support for a wide range of use cases. Spring for GraphQL is the successor of the GraphQL Java Spring project from the GraphQL Java team. It aims to be the foundation for all Spring, GraphQL applications. -
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macOS Security Compliance
macOS Security Compliance Project
...The configuration settings in this document were derived from National Institute of Standards and Technology (NIST) Special Publication (SP) 800-53, Security and Privacy Controls for Information Systems and Organizations, Revision 5. This is a joint project of federal operational IT Security staff from the National Institute of Standards and Technology (NIST), National Aeronautics and Space Administration (NASA), Defense Information Systems Agency (DISA), and Los Alamos National Laboratory (LANL). -
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Openwifi
open-source IEEE 802.11 WiFi baseband FPGA (chip) design
Linux mac80211 compatible full-stack IEEE802.11/Wi-Fi design based on SDR (Software Defined Radio). -
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MuJoCo
Multi-Joint dynamics with Contact. A general purpose physics simulator
MuJoCo, developed and maintained by Google DeepMind, is a high-performance physics engine designed for simulating complex, articulated systems that interact through contact. It is widely used in research fields such as robotics, biomechanics, computer graphics, animation, and machine learning, where fast and accurate physics simulations are essential. The engine provides a robust C API optimized for real-time computation, making it suitable for scientific research and advanced simulation... -
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JEPA
PyTorch code and models for V-JEPA self-supervised learning from video
JEPA (Joint-Embedding Predictive Architecture) captures the idea of predicting missing high-level representations rather than reconstructing pixels, aiming for robust, scalable self-supervised learning. A context encoder ingests visible regions and predicts target embeddings for masked regions produced by a separate target encoder, avoiding low-level reconstruction losses that can overfit to texture. -
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Must Reading on ISAC
Must Reading Papers, Research Library, Open-Source Code
A design paradigm and corresponding enabling technologies, in which sensing and comms systems are integrated to efficiently utilize congested wireless/hardware resources, and even to pursue mutual benefits. Waveform Design and Signal Processing Aspects for Fusion of Wireless Communications and Radar Sensing. Supported by IEEE ComSoc ISAC Emerging Technology Initiative (ETI). Integrating Sensing and Communications for Ubiquitous IoT. Include reproducible codes, good papers and a research libary. -
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LeWorldModel
Official code base for LeWorldModel: Stable End-to-End Joint-Embedding
LeWorldModel is a minimalist tiling window manager designed for the X11 windowing system, focusing on simplicity, performance, and efficient use of screen space. It provides automatic window tiling behavior, organizing application windows into structured layouts without requiring manual resizing or positioning. The project emphasizes a lightweight design, minimizing resource usage while maintaining responsiveness and stability. It is highly configurable through source code or configuration... -
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DeepSeek VL2
Mixture-of-Experts Vision-Language Models for Advanced Multimodal
DeepSeek-VL2 is DeepSeek’s vision + language multimodal model—essentially the next-gen successor to their first vision-language models. It combines image and text inputs into a unified embedding / reasoning space so that you can query with text and image jointly (e.g. “What’s going on in this scene?” or “Generate a caption appropriate to context”). The model supports both image understanding (vision tasks) and multimodal reasoning, and is likely used as a component in agent systems to... -
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LÖVR
Lua Virtual Reality engine
An open-source framework for rapidly building immersive 3D experiences. You can use LÖVR to easily create VR experiences without much setup or programming experience. The framework is tiny, fast, open-source, and supports lots of different platforms and devices. Runs on Windows, Mac, Linux, Android, WebXR. Supports Vive/Index, Oculus Rift/Quest, Pico, Windows MR, and has a VR simulator. Simple VR scenes can be created in just a few lines of Lua. Writen in C99 and scripted with LuaJIT,... -
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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video2robot
End-to-end pipeline converting generative videos
...The pipeline supports both prompt-to-video generation using models like Veo/Sora and video upload processing, followed by human pose extraction through a 3D pose model and retargeting of that motion to robot joints using a general motion retargeting system. This workflow allows users to generate robot motion files that specify joint angles, root positions, and orientations that can be deployed on supported robot platforms (e.g., Unitree models). Video2robot includes scripts for each stage of the pipeline (generation, extraction, conversion, visualization) and can run as a CLI or through a basic web UI. -
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ClimateTools.jl
Climate science package for Julia
Climate analysis tools in Julia. ClimateTools.jl is a collection of commonly-used tools in Climate science. Basics of climate field analysis are covered, with some forays into exploratory techniques associated with climate scenario design. The package is aimed to ease the typical steps of analysis of climate models outputs and gridded datasets (support for weather stations is a work-in-progress). Climate indices and bias correction functions are coded to leverage the use of multiple threads.... -
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lolcommits
git-based selfies for software developers
...The single most useful piece of software known to mankind. Animate your progress through a project and watch as you age. See what you looked like when you broke the build. Keep a joint lolrepository for your entire company. Lolcommits allows a growing list of plugins to perform additional work on your lolcommit image after capturing. Thanks to the great open-source community, lolcommits now works on MacOSX, Linux, and even Windows. Configure lolcommits to generate an animated GIF with each commmit for extra lulz! ... -
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GDINA Package for Cognitively Diagnostic
Package for Cognitively Diagnostic Analyses
...Estimating the diagnostic tree model (experimental). Estimating multiple-choice models. Modelling independent, saturated, higher-order, loglinear smoothed, and structured joint attribute distribution. Accommodating multiple-group model analysis. Imposing monotonic constrained success probabilities. Accommodating binary and polytomous attributes. -
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Mesh R-CNN
code for Mesh R-CNN, ICCV 2019
Mesh R-CNN is a 3D reconstruction and object understanding framework developed by Facebook Research that extends Mask R-CNN into the 3D domain. Built on top of Detectron2 and PyTorch3D, Mesh R-CNN enables end-to-end 3D mesh prediction directly from single RGB images. The model learns to detect, segment, and reconstruct detailed 3D mesh representations of objects in natural images, bridging the gap between 2D perception and 3D understanding. Unlike voxel-based or point-based approaches, Mesh... -
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TensorFlow Serving
Serving system for machine learning models
TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. It deals with the inference aspect of machine learning, taking models after training and managing their lifetimes, providing clients with versioned access via a high-performance, reference-counted lookup table. TensorFlow Serving provides out-of-the-box integration with TensorFlow models, but can be easily extended to serve other types of models and data. The... -
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...this config was put together by Extravis developerю Extravis https://github.com/Extravi/Installer/releases Who assembled the config or developed it https://github.com/Extravi If you want to do some joint development or have questions for me, you can join my Discord server https://discord.gg/fQSchwjXzM
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ProbabilisticCircuits.jl
Probabilistic Circuits from the Juice library
...Probabilistic Circuits provides a unifying framework for several family of tractable probabilistic models. PCs are represented as computational graphs that define a joint probability distribution as recursive mixtures (sum units) and factorizations (product units) of simpler distributions (input units). Given certain structural properties, PCs enable different range of tractable exact probabilistic queries such as computing marginals, conditionals, maximum a posteriori (MAP), and more advanced probabilistic queries. -
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Matter.js
A 2D rigid body physics engine for the web
Matter.js is a flexible 2D rigid body physics engine written in JavaScript, designed for use in web applications to simulate real-world physical interactions within the browser. It offers a comprehensive set of physics features like gravity, collisions, restitution (bounces), friction, sleeping bodies, and constraint systems that enable developers to build interacting objects with realistic motion. This engine can power everything from dynamic interactive animations and educational... -
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Wikipedia2Vec
A tool for learning vector representations of words and entities
Wikipedia2Vec is an embedding learning tool that creates word and entity vector representations from Wikipedia, enabling NLP models to leverage structured and contextual knowledge. -
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CAMPARI
Software for molecular simulations and trajectory analysis
...We have added a number of new features, most notably a Python interface for interpreting user-supplied code (with the help of ForPy), a novel trajectory storage standard (with the help of libpqxx/PostgreSQL), and a module for performing transition path theory. Naturally, CAMPARI continues to provide the reference implementation of the ABSINTH force field paradigm and implicit solvation model. CAMPARI is a joint package for performing and analyzing molecular simulations, in particular of systems of biological relevance. It focuses on a wide availability of algorithms for (advanced) sampling and is capable of combining Monte Carlo and molecular dynamics in seamless fashion. CAMPARI offers the user a very high level of control over all implemented features. ... -
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CoTracker
CoTracker is a model for tracking any point (pixel) on a video
CoTracker is a learning-based point tracking system that jointly follows many user-specified points across a video, rather than tracking each point independently. By reasoning about all tracks together, it can maintain temporal consistency, handle mutual occlusions, and reduce identity swaps when trajectories cross. The model takes sparse point queries on one frame and predicts their sub-pixel locations and a visibility score for every subsequent frame, producing long, coherent trajectories.... -
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MetaTransformer
Meta-Transformer for Unified Multimodal Learning
We're thrilled to present OneLLM, an ensembling Meta-Transformer framework with Multimodal Large Language Models, which performs multimodal joint training, supports more modalities including fMRI, Depth, and Normal Maps, and demonstrates very impressive performances on 25 benchmarks. -
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iJEPA
Official codebase for I-JEPA
i-JEPA (Image Joint-Embedding Predictive Architecture) is a self-supervised learning framework that predicts missing high-level representations rather than reconstructing pixels. A context encoder sees visible regions of an image and predicts target embeddings for masked regions produced by a slowly updated target encoder, focusing learning on semantics instead of texture. -
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Hyperformer
Hypergraph Transformer for Skeleton-based Action Recognition
This is the official implementation of our paper "Hypergraph Transformer for Skeleton-based Action Recognition." Skeleton-based action recognition aims to recognize human actions given human joint coordinates with skeletal interconnections. By defining a graph with joints as vertices and their natural connections as edges, previous works successfully adopted Graph Convolutional networks (GCNs) to model joint co-occurrences and achieved superior performance. More recently, a limitation of GCNs is identified, i.e., the topology is fixed after training. ... -
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FrankMocap
A Strong and Easy-to-use Single View 3D Hand+Body Pose Estimator
FrankMocap is a monocular 3D human capture system that estimates body, hand, and optionally face pose from a single RGB image or video. It regresses parametric human models (e.g., SMPL/SMPL-X) directly, producing temporally stable meshes and joint angles suitable for animation or analytics. The pipeline couples a robust 2D keypoint detector with 3D mesh regression networks and priors that keep results anatomically plausible. It can run frame-by-frame or with temporal smoothing, and includes demo apps for live webcam capture as well as batch processing. Outputs include textured meshes, joint locations, and model parameters that can be exported to common DCC tools and game engines. ...
