Algo-burn - Script to configure an Algorand address as a "burn" address for one or more ASA tokens

Overview

Algorand Burn Address

This is a simple script to illustrate how a "burn address" can be generated for one or more Algorand Standard Assets (ASAs). Since addresses need to be "opted-in" to receive a specific ASA, it is not possible to just send tokens to a random address.

The script uses a similar approach to what the Algorand Foundation used to burn Early Redemption Auction Tokens. All details can be found here: https://algorand.foundation/news/the-algorand-foundation-today-confirm-token-burn

Since we are not burning ALGOs (that would require to only send ALGOs to a random address) we are using a process called 're-keying' so that, once the burn address is configured accordingly, its "authorized account" is set to a random public address.

Process is as follow:

  • Fund a "burn" address with the amount of Algo required to opt-into one or more ASAs
  • Add a ".env" file with burn address and its mnemonic key
  • Pick a list of assets you want to burn and modify the script accordingly
  • Pick a date and modify the script accordingly. This is used to generate a random (verifiable) address
  • Run the code

If code is successful and you run it twice, it will fail the second time since you don't have the authorization to sign transactions for the burn address anymore.

This has been used to generate a burn address for AlgoScout (ASA ID: 569120128)

Here are the links to the SCOUT opt-in and re-keying transactions:

https://algoexplorer.io/tx/FDSOCKQPB7CM67XJJOT62Y7C3QZXA2BGYIT7JM5FKM7WXAHAGEVA https://algoexplorer.io/tx/DTLKTFMO4WKYB7LDIDYORA6TFJPD7LZWMDMEYHEIFMOI6PAR3C4A

NOTE: burnt tokens will still be included in the total circulating supply calculation.

MakeItTalk: Speaker-Aware Talking-Head Animation

MakeItTalk: Speaker-Aware Talking-Head Animation This is the code repository implementing the paper: MakeItTalk: Speaker-Aware Talking-Head Animation

Adobe Research 285 Jan 08, 2023
ICCV2021 Paper: AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection

ICCV2021 Paper: AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection

Zongdai 107 Dec 20, 2022
CVPR 2022 "Online Convolutional Re-parameterization"

OREPA: Online Convolutional Re-parameterization This repo is the PyTorch implementation of our paper to appear in CVPR2022 on "Online Convolutional Re

Mu Hu 121 Dec 21, 2022
RL-GAN: Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation

RL-GAN: Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation RL-GAN is an official implementation of the paper: T

42 Nov 10, 2022
GPU Accelerated Non-rigid ICP for surface registration

GPU Accelerated Non-rigid ICP for surface registration Introduction Preivous Non-rigid ICP algorithm is usually implemented on CPU, and needs to solve

Haozhe Wu 144 Jan 04, 2023
This is the repo of the manuscript "Dual-branch Attention-In-Attention Transformer for speech enhancement"

DB-AIAT: A Dual-branch attention-in-attention transformer for single-channel SE

Guochen Yu 68 Dec 16, 2022
These are the materials for the paper "Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations"

Few-shot-NLEs These are the materials for the paper "Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations". You can find the smal

Yordan Yordanov 0 Oct 21, 2022
It's final year project of Diploma Engineering. This project is based on Computer Vision.

Face-Recognition-Based-Attendance-System It's final year project of Diploma Engineering. This project is based on Computer Vision. Brief idea about ou

Neel 10 Nov 02, 2022
harmonic-percussive-residual separation algorithm wrapped as a VST3 plugin (iPlug2)

Harmonic-percussive-residual separation plug-in This work is a study on the plausibility of a sines-transients-noise decomposition inspired algorithm

Derp Learning 9 Sep 01, 2022
CS506-Spring2022 - Code and Slides for Boston University CS 506

CS 506 - Computational Tools for Data Science Code, slides, and notes for Boston

Lance Galletti 17 May 06, 2022
CNN Based Meta-Learning for Noisy Image Classification and Template Matching

CNN Based Meta-Learning for Noisy Image Classification and Template Matching Introduction This master thesis used a few-shot meta learning approach to

Kumar Manas 2 Dec 09, 2021
Convolutional Neural Networks

Darknet Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation. D

Joseph Redmon 23.7k Jan 05, 2023
Machine learning for NeuroImaging in Python

nilearn Nilearn enables approachable and versatile analyses of brain volumes. It provides statistical and machine-learning tools, with instructive doc

919 Dec 25, 2022
A library for graph deep learning research

Documentation | Paper [JMLR] | Tutorials | Benchmarks | Examples DIG: Dive into Graphs is a turnkey library for graph deep learning research. Why DIG?

DIVE Lab, Texas A&M University 1.3k Jan 01, 2023
PyTorch original implementation of Cross-lingual Language Model Pretraining.

XLM NEW: Added XLM-R model. PyTorch original implementation of Cross-lingual Language Model Pretraining. Includes: Monolingual language model pretrain

Facebook Research 2.7k Dec 27, 2022
This is an open source python repository for various python tests

Welcome to Py-tests This is an open source python repository for various python tests. This is in response to the hacktoberfest2021 challenge. It is a

Yada Martins Tisan 3 Oct 31, 2021
Optimal Adaptive Allocation using Deep Reinforcement Learning in a Dose-Response Study

Optimal Adaptive Allocation using Deep Reinforcement Learning in a Dose-Response Study Supplementary Materials for Kentaro Matsuura, Junya Honda, Imad

Kentaro Matsuura 4 Nov 01, 2022
A medical imaging framework for Pytorch

Welcome to MedicalTorch MedicalTorch is an open-source framework for PyTorch, implementing an extensive set of loaders, pre-processors and datasets fo

Christian S. Perone 799 Jan 03, 2023
Pytorch implementation of Supporting Clustering with Contrastive Learning, NAACL 2021

Supporting Clustering with Contrastive Learning SCCL (NAACL 2021) Dejiao Zhang, Feng Nan, Xiaokai Wei, Shangwen Li, Henghui Zhu, Kathleen McKeown, Ram

231 Jan 05, 2023
Simple improvement of VQVAE that allow to generate x2 sized images compared to baseline

vqvae_dwt_distiller.pytorch Simple improvement of VQVAE that allow to generate x2 sized images compared to baseline. It allows to generate 512x512 ima

Sergei Belousov 25 Jul 19, 2022