Python

Memory location matters for performance

If you’re writing high-performance code, what matters is how many CPU instructions you run. Right? That’s what I learned long ago in school, anyway, when I took algorithms and data structures classes. The basic assumption was that reading or writing to memory took a small, constant amount of time, and so you could focus on how many operations were being done. This was so fundamental to how we were taught that we didn’t even have to write real code: pseudo-code […]

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Bot

The stable version of iso6.9

iso.bot is originally made by notsniped#4573. This is a remix of iso.bot by αrchιshα#5518. iso6.9 is a Discord bot written in Python and is used to make your Discord experience better! It is similar to iso.bot as it is a remix. Its prefix is ]. Invite link: https://discord.com/oauth2/authorize?client_id=915488087554002956&permissions=8&scope=bot (it is normally run in replit) To run it, put the bot token inside: token = “token” Also put your discord id in: cid = “client_id” To get your id, run ?userinfo […]

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Scripts

Protein hallucination and inpainting with RoseTTAFold

Jue Wang ([email protected])Doug Tischer ([email protected])Sidney Lisanza ([email protected])David Juergens ([email protected])Joe Watson ([email protected]) This repository contains code for protein hallucination or inpainting, asdescribed in ourpreprint. Codefor postprocessing and analysis scripts included in scripts/. License All code is released under the MIT license. All weights for neural networks are released for non-commercial use only under the Rosetta-DL license. Installation Clone the repository: git clone https://git.ipd.uw.edu/jue/rfdesign.git cd rfdesign Create environment and install dependencies: cd envs conda env create -f SE3.yml    

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Render

Volume Rendering of Neural Implicit Surfaces

Project Page | Paper | Data This repository contains an implementation for the NeurIPS 2021 paper:Volume Rendering of Neural Implicit Surfaces Lior Yariv1, Jiatao Gu2, Yoni Kasten1, Yaron Lipman1,21Weizmann Institute of Science, 2Facebook AI Research The paper introduce VolSDF: a volume rendering framework for implicit neural surfaces, allowing to learn high fidelity geometry from a sparse set of input images. Setup Installation Requirmenets The code is compatible with python 3.8 and pytorch 1.7. In addition, the following packages are required:numpy, […]

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Parser

A parser of Windows Defender’s DetectionHistory forensic artifact, containing substantial info about quarantined files and executables

The files parsed by this application may be found on any Windows system, if they exist, under [root]ProgramDataMicrosoftWindows DefenderScansHistoryServiceDetectionHistory[numbered folder][File GUID] NOTES The file header should be of the form: b’0800000008′, or else it is not a valid DetectionHistory file. Immediately following the file header and before the first mention of “Magic Version”, the GUID of the file is given in Big-Endian(?) representation, capped off by a b’24’ at the end, signaling the end of the GUID and beginning of […]

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Discord

A Discord bot made with Pycord

A Discord bot made with Pycord Features Not much lol • Easy to use commands• Moderation capabilities• Fun commands• Utility Usage The Default prefix is . . though you can find the server speific by pinging the bot. and you can also change it by .change_prefix {new_prefix}Use .help for a full list of commands. Get Add Yuu to your Server GitHub View Github    

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DNS

Analyze your DNS traffic super easy

___ _ ___ _ _ ___ | __|__| |_ ___| | | / __| | _|/ _| ‘ / _ |) | .` __ |_____|_||____/___/|_|_|___/ v0.0.1 Analyze DNS traffic. EchoDNS shows all requested DNS traffic. It will forward the original packet to a real DNS server. You can choose the target DNS. Default is 1.1.1.1.The endpoint won’t notice this “FakeDNS” at all. With the usage of multithreading, EchoDNS will perfom requests very efficient. A builtin garbage collector takes care of […]

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Dataset

PyTorch image dataloaders and utility functions to load datasets for supervised continual learning

Introduction This repository contains PyTorch image dataloaders and utility functions to load datasets for supervised continual learning. Currently supported datasets: MNIST Pairwise-MNIST Fashion-MNIST not-MNIST (letters version of MNIST, see EMNIST for more detail) CIFAR-10 CIFAR-100 German Traffic Signs Street View House Numbers (SVHN) Incremental CIFAR-100 Incremental TinyImageNet Features The provided interface simplifies typical data loading for supervised continual learning scenarios. Dataset order, additional training data (for replay buffers) and test data (for global metrics computation) can all be specified. A […]

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App

Creation of an application in django on music albums

Creation of an application in django on music albums Before Before you must verify that you have the latest python version downloaded In your terminalfirst create a virtual environment In windows In Unix o MacOS run source envScriptsactivate.bat Run the command pip install -r requirements.txt Create an .env file in the same location as the manage.py file inside the .env file – DJANGO_SETTINGS_MODULE =    

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