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Matter (formerly Project CHIP) creates more connections between more objects, simplifying development for manufacturers and increasing compatibility for consumers, guided by the Connectivity Standards Alliance.

A Wayland compositor designed for daily use, with scrolling, dwindle, master, and stacking layouts, per-output workspaces, window rules, blur, shadows, and fluid animations.

A coupling library and ecosystem for partitioned multi-physics and multi-scale simulations, including surface and volume coupling.

  • multi-physics
  • fluid-structure-interaction
  • high-performance-computing
  • conjugate-heat-transfer

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.