McGill Physics Hackathon 2021: Reaction-Diffusion Models for the Generation of Biological Patterns

Overview

DiffuseAnimals: Reaction-Diffusion Models for the Generation of Biological Patterns

Logo

Introduction

Reaction-diffusion equations can be utilized in order to describe processes of non-chemical systems as well as those containing one or more chemical substances that exhibit diverse spatiotemporal patterns such as socioeconomic interactions, neutron diffusion, and biochemical reactions. Solutions to these equations can exhibit wave-like behavior as first described by Alan Turing in his 1952 article The Chemical Basis of Morphogenesis. Here, we explore the Gray-Scott and FitzHugh–Nagumo models in order to simulate the diversity of patterns found among species as observed on the skin, scales, and fur of animals and across the domains of life.

McGill Physics Hackathon 2021 Team Members:

  • Ryan Senoune
  • Austin J. Szuminsky
  • Yuliya Shpunarska
  • Xavier L'Heureux

License

MIT

Owner
Austin Szuminsky
Austin Szuminsky
Building Ellee — A GPT-3 and Computer Vision Powered Talking Robotic Teddy Bear With Human Level Conversation Intelligence

Using an object detection and facial recognition system built on MobileNetSSDV2 and Dlib and running on an NVIDIA Jetson Nano, a GPT-3 model, Google Speech Recognition, Amazon Polly and servo motors,

24 Oct 26, 2022
City Surfaces: City-scale Semantic Segmentation of Sidewalk Surfaces

City Surfaces: City-scale Semantic Segmentation of Sidewalk Surfaces Paper Temporary GitHub page for City Surfaces paper. More soon! While designing s

14 Nov 10, 2022
A new play-and-plug method of controlling an existing generative model with conditioning attributes and their compositions.

Viz-It Data Visualizer Web-Application If I ask you where most of the data wrangler looses their time ? It is Data Overview and EDA. Presenting "Viz-I

NVIDIA Research Projects 66 Jan 01, 2023
(SIGIR2020) “Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback’’

Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback About This repository accompanies the real-world experiments conducted i

yuta-saito 19 Dec 01, 2022
Streamlit component for TensorBoard, TensorFlow's visualization toolkit

streamlit-tensorboard This is a work-in-progress, providing a function to embed TensorBoard, TensorFlow's visualization toolkit, in Streamlit apps. In

Snehan Kekre 27 Nov 13, 2022
This repository includes code of my study about Asynchronous in Frequency domain of GAN images.

Exploring the Asynchronous of the Frequency Spectra of GAN-generated Facial Images Binh M. Le & Simon S. Woo, "Exploring the Asynchronous of the Frequ

4 Aug 06, 2022
A general framework for deep learning experiments under PyTorch based on pytorch-lightning

torchx Torchx is a general framework for deep learning experiments under PyTorch based on pytorch-lightning. TODO list gan-like training wrapper text

Yingtian Liu 6 Mar 17, 2022
CVNets: A library for training computer vision networks

CVNets: A library for training computer vision networks This repository contains the source code for training computer vision models. Specifically, it

Apple 1.1k Jan 03, 2023
Source code for Task-Aware Variational Adversarial Active Learning

Contrastive Coding for Active Learning under Class Distribution Mismatch Official PyTorch implementation of ["Contrastive Coding for Active Learning u

27 Nov 23, 2022
Asynchronous Advantage Actor-Critic in PyTorch

Asynchronous Advantage Actor-Critic in PyTorch This is PyTorch implementation of A3C as described in Asynchronous Methods for Deep Reinforcement Learn

Reiji Hatsugai 38 Dec 12, 2022
Official implementation of the paper 'Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution'

DASR Paper Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution Jie Liang, Hui Zeng, and Lei Zhang. In arxiv preprint. Abs

81 Dec 28, 2022
A Runtime method overload decorator which should behave like a compiled language

strongtyping-pyoverload A Runtime method overload decorator which should behave like a compiled language there is a override decorator from typing whi

20 Oct 31, 2022
Backend code to use MCPI's python API to make infinite worlds with custom generation

inf-mcpi Backend code to use MCPI's python API to make infinite worlds with custom generation Does not save player-placed blocks! Generation is still

5 Oct 04, 2022
Adds timm pretrained backbone to pytorch's FasterRcnn model

Operating Systems Lab (ETCS-352) Experiments for Operating Systems Lab (ETCS-352) performed by me in 2021 at uni. All codes are written by me except t

Mriganka Nath 12 Dec 03, 2022
Official PyTorch implementation of the paper: DeepSIM: Image Shape Manipulation from a Single Augmented Training Sample

DeepSIM: Image Shape Manipulation from a Single Augmented Training Sample (ICCV 2021 Oral) Project | Paper Official PyTorch implementation of the pape

Eliahu Horwitz 393 Dec 22, 2022
Efficient Two-Step Networks for Temporal Action Segmentation (Neurocomputing 2021)

Efficient Two-Step Networks for Temporal Action Segmentation This repository provides a PyTorch implementation of the paper Efficient Two-Step Network

8 Apr 16, 2022
A highly modular PyTorch framework with a focus on Neural Architecture Search (NAS).

UniNAS A highly modular PyTorch framework with a focus on Neural Architecture Search (NAS). under development (which happens mostly on our internal Gi

Cognitive Systems Research Group 19 Nov 23, 2022
Adaptation through prediction: multisensory active inference torque control

Adaptation through prediction: multisensory active inference torque control Submitted to IEEE Transactions on Cognitive and Developmental Systems Abst

Cristian Meo 1 Nov 07, 2022
Dieser Scanner findet Websites, die nicht direkt in Suchmaschinen auftauchen, aber trotzdem erreichbar sind.

Deep Web Scanner Dieses Script findet Websites, die per IPv4-Adresse erreichbar sind und speichert deren Metadaten. Die Ausgabe im Terminal wird nach

Alex K. 30 Nov 18, 2022
CMSC320 - Introduction to Data Science - Fall 2021

CMSC320 - Introduction to Data Science - Fall 2021 Instructors: Elias Jonatan Gonzalez and José Manuel Calderón Trilla Lectures: MW 3:30-4:45 & 5:00-6

Introduction to Data Science 6 Sep 12, 2022