These are NOT product / license keys that are valid for Windows activation.
These keys only select the edition of Windows to install during setup, but they do not activate or license the installation.
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| #!/usr/bin/env bash | |
| # setup.sh - Bootstrap Lotus 1-2-3 on macOS arm64 via Blink | |
| # Authored by Jim Huang <jserv@ccns.ncku.edu.tw> | |
| # | |
| # Downloads the prebuilt i386 binary from GitHub, builds Blink, | |
| # populates the sysroot from Ubuntu archive i386 .debs, validates the layout, | |
| # and generates run-123.sh. No SSH/scp needed -- everything from public URLs. | |
| # | |
| set -euo pipefail |
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| pkg/ | |
| src/ | |
| understand-bin-*.pkg.* | |
| Understand-*.tgz | |
| .SRCINFO |
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| """ | |
| Smooth Transition Autoregression | |
| References | |
| ---------- | |
| Dijk, Dick van, Timo Terasvirta, and Philip Hans Franses. 2002. | |
| "Smooth Transition Autoregressive Models - a Survey of Recent Developments." | |
| Econometric Reviews 21 (1): 1-47. |
Note
Obligatory disclaimer: this is for educational purposes only. I am not responsible for any damages caused by following this guide, or using any of the script(s) herein.
This guide prioritizes arm64 macOS, but may also work for other platforms.
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| // Configuration object for the search | |
| const searchConfig = { | |
| searchCriteria: { user: { id: true, email: true } }, // What we're looking for | |
| maxDepth: 50, // How deep to search in the tree | |
| stopAfterFirst: false, // Whether to stop after finding the first match | |
| searchPaths: ["memoizedProps", "memoizedState"], // Where to look in each node | |
| mainSelector: "#__next", // The root element of our React app | |
| callback: (matchingObjects) => { | |
| matchingObjects.forEach(({ matchingObject, fiberNode }) => { | |
| console.log("Found matching object:", matchingObject); |
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| [ | |
| { | |
| "url": "https://updates.cdn-apple.com/2023SummerSeed/patches/042-21091/60572AF0-9BC3-465F-89ED-77117194CB26/com_apple_MobileAsset_SoftwareUpdate/3df121022bd578846478faa25a4dcf3055396954.zip", | |
| "build": "21N5207g", | |
| "filename": "iBEC.n301.RELEASE.im4p", | |
| "kbag": "AB7893B981E44BFF298328C89C826F8BA6EF1A7ADC80DB156C9D55D9F4E27E8AD2CC21AFA42A41E1392B57E9FE90D992", | |
| "key": "34b218667cd03eb93e073b9b3bca4a865b20f130550a800b2aa2c1c2348041865cee47db7e3bcda739d05adde9f9f716" | |
| }, | |
| { | |
| "url": "https://updates.cdn-apple.com/2023SummerSeed/patches/042-21091/60572AF0-9BC3-465F-89ED-77117194CB26/com_apple_MobileAsset_SoftwareUpdate/3df121022bd578846478faa25a4dcf3055396954.zip", |
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| """Example of adaptive Lasso to produce event sparser solutions | |
| Adaptive lasso consists in computing many Lasso with feature | |
| reweighting. It's also known as iterated L1. | |
| """ | |
| # Authors: Alexandre Gramfort <firstname.lastname@inria.fr> | |
| # | |
| # License: BSD (3-clause) | |
| import numpy as np |
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| """ | |
| Self-Exciting Threshold Autoregression | |
| References | |
| ---------- | |
| Hansen, Bruce. 1999. | |
| "Testing for Linearity." | |
| Journal of Economic Surveys 13 (5): 551-576. | |
| """ |
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