Single Red Blood Cell Hydrodynamic Traps Via the Generative Design

Last update: Jun 16, 2022

Overview

Single Red Blood Cell Hydrodynamic Traps Via the Generative Design

This is the repository for the paper "Single Red Blood Cell Hydrodynamic Traps Via the Generative Design" submitted to Micromachines journal.

In the paper, the evolutionary approach implemented in GEFEST framework was applied to optimize the structure of geometrically-encoded cell traps.

Experiments folder contains the scripts that run the optimization for the described case. Setup contains the wrapper for the COMSOL simulator and the configuration for the simulation environment. Results folder contains the structure of best obtained configuration.

Owner

Natural Systems Simulation Lab

Natural Systems Simulation Lab in ITMO University

GitHub Repository

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Single Red Blood Cell Hydrodynamic Traps Via the Generative Design

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Overview

Single Red Blood Cell Hydrodynamic Traps Via the Generative Design

Owner

Natural Systems Simulation Lab

Our VMAgent is a platform for exploiting Reinforcement Learning (RL) on Virtual Machine (VM) scheduling tasks.

🔮 Execution time predictions for deep neural network training iterations across different GPUs.

LAnguage Model Analysis

Learning Open-World Object Proposals without Learning to Classify

A trashy useless Latin programming language written in python.

Kaggle | 9th place (part of) solution for the Bristol-Myers Squibb – Molecular Translation challenge

Memory Defense: More Robust Classificationvia a Memory-Masking Autoencoder

Bayesian optimisation library developped by Huawei Noah's Ark Library

[TIP 2020] Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion

Feed forward VQGAN-CLIP model, where the goal is to eliminate the need for optimizing the latent space of VQGAN for each input prompt

Implementation of Graph Convolutional Networks in TensorFlow

Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation

pytorch implementation of Attention is all you need

Python implementation of NARS (Non-Axiomatic-Reasoning-System)

Official PyTorch implemention of our paper "Learning to Rectify for Robust Learning with Noisy Labels".

Dirty Pixels: Towards End-to-End Image Processing and Perception

The codes of paper 'Active-LATHE: An Active Learning Algorithm for Boosting the Error exponent for Learning Homogeneous Ising Trees'

SHIFT15M: multiobjective large-scale fashion dataset with distributional shifts

Code for "Retrieving Black-box Optimal Images from External Databases" (WSDM 2022)

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