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Discover QUBO.jl, a Julia package bridging quantum optimization and JuMP, enabling seamless access to quantum devices and automatic model reformulation for QUBO problems.
An overview of Julia’s ecosystem for scalable scientific computing on supercomputers, CPUs, GPUs, and other heterogeneous hardware.
Researchers share how Julia supports astronomical simulations, inference, adaptive optics, and radio astronomy workflows.
Learn to build interactive and static data visualizations in Julia with Makie, including layouts, theming, custom recipes, scene graphs, interactions, and interactive app projects.
Explore Markov decision processes through a Julia grid-world implementation using value iteration, reinforcement learning, and Monte Carlo tree search.
Build a Julia web application with Genie.jl, covering MVC structure, database migrations, templating, search, REST APIs, deployment, and authentication.
A perspective on how Julia’s abstraction and metaprogramming support biological computing, from single-cell analysis and image processing to whole-cell modeling.
Simulate chemical mechanisms in Julia with ReactionMechanismSimulator.jl, exploring mass-action kinetics, sensitivity analysis, symbolic reduction, and steady-state identification.
A keynote on PyTorch’s implementation, ecosystem, open-source philosophy, and evolution from Torch.
Explore algebraic metaprogramming in Julia through e-graphs, equality saturation, and classical term rewriting with Metatheory.jl.
Dagger.jl extends Julia parallelism with scheduling, distributed arrays, checkpointing, GPU execution, and a security-camera monitoring demo.
A keynote on using hierarchical low-rank linear algebra and machine learning to accelerate large-scale solvers and tune their performance on high-performance computers.
A tour of new Julia VS Code extension features, including native Jupyter notebooks, faster debugging, REPL connections, progress logging, and package releases.
A data-driven survey of Julia’s General registry examines package size, licenses, documentation, tests, CI, contributors, and analysis with PackageAnalyzer.jl.
A Julia-based approach connects nonlinear-dynamics mathematics with executable code, figures, interactive applications, and practice.
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