Learn how to quickly create a new terminal in Jupyter using a custom keyboard shortcut. This is especially useful for developers who frequently need to run shell commands while working in Jupyter notebooks.
A comprehensive Docker setup for bioinformatics work, featuring non-root user configuration, Python virtual environments, R with Seurat, and Jupyter Lab with multi-profile docker-compose support.
The term ‘HAL’ means different things in different domains—from kernel-level platform abstraction to application-level sensor control. Understanding these differences helps clarify domain-specific architecture choices.
GraphHopper makes a deliberate design choice: paths are represented by edges, not nodes. This matters for correctness (turn restrictions/costs), fidelity (multiple parallel edges between the same nodes), and efficiency (edge properties drive routing).
Python demo of Figure 2.16 Sparse Error Correction via Logan’s Phenomenon from High-Dimensional Data Analysis with Low-Dimensional Models - John Wright, Yi Ma, Page 65
Python implementation of Algorithm L1-Minimization by Projected Subgradient from High-Dimensional Data Analysis with Low-Dimensional Models - John Wright, Yi Ma, Page 63
Let \(A\) be a matrix with size \(m \times n\). We want to project point z onto a plane \(\begin{align} \arg \min_{x} \lVert z-x \rVert_2 \\ \text{s.t. } Ax = y \end{align}\)
Python implementation of Algorithm L0-Minimization by Exhaustive Search from High-Dimensional Data Analysis with Low-Dimensional Models - John Wright, Yi Ma, Page 48