Images

A Python library for processing images of image-based spatial transcriptomics

starfish starfish: scalable pipelines for image-based transcriptomics starfish is a Python library for processing images of image-based spatial transcriptomics. It lets you build scalable pipelines that localize and quantify RNA transcripts in image data generated by any FISH method, from simple RNA single-molecule FISH to combinatorial barcoded assays. If you want to give it a try, the quick start tutorial will guide you from installation to running a pipeline in under 10 minutes. For more comprehensive instructions on how to […]

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Security

A Vulnerability Scanner & Auto Exploiter You can use this tool to check the security

Vulnnr create a target list or select one target, scans then exploits, done!Vulnnr is a Vulnerability Scanner & Auto Exploiter You can use this tool to check the security by finding the vulnerability in your website or you can use this tool to Get Shells Offers LFI Scanners > Coming soon XSS Scanners > Working SQLI injection scanners > Working Domain Scanner > Using hackerone API/finds subdoamins CMS detector > Working Server detector > Working Common vulnerable files Scanner > […]

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Advanced, Machine Learning, NLP, Python, Text, Topic Modeling, Unstructured Data

Topic Modelling in Natural Language Processing

Introduction Natural language processing is the processing of languages used in the system that exists in the library of nltk where this is processed to cut, extract and transform to new data so that we get good insights into it. It uses only the languages that exist in the library because NLP-related things exist there itself so it cannot understand the things beyond what is present in it. If you do processing on another language then you have to add […]

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Python

The hidden performance overhead of Python C extensions

Python is slow, and compiled languages like Rust, C, or C++ are fast. So when your application is too slow, rewriting some of your code in a compiled extension can seem like the natural approach to speeding things up. Unfortunately, compiled extensions are sometimes actually slower than the equivalent Python code. And even when they’re faster, the performance improvement might be far less than you’d imagine, due to hidden overhead caused by two factors: Function call overhead. Serialization/deserialization overhead. Let’s […]

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Data Visualization

A Python library for electronic structure pre/post-processing

PyProcar PyProcar is a robust, open-source Python library used for pre- and post-processing of the electronic structure data coming from DFT calculations. PyProcar provides a set of functions that manage data obtained from the PROCAR format. Basically, the PROCAR format is a projection of the Kohn-Sham states over atomic orbitals. That projection is performed to every k-point in the considered mesh, every energy band and every atom. PyProcar is capable of performing a multitude of tasks including plotting plain and […]

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Data Analysis

Histogramming for analysis powered by boost-histogram

Hist Hist is a analyst friendly front-end for boost-histogram, designed for Python 3.7+ (3.6 users get version 2.3). Installation You can install this library from PyPI with pip: python3 -m pip install “hist[plot]” If you do not need the plotting features, you can skip the [plot] extra. Features Hist currently provides everything boost-histogram provides, and the following enhancements: Hist augments axes with names: name= is a unique label describing each axis label= is an optional string that is used in […]

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Machine Learning

Self-Supervised Learning for General-Purpose Audio Representation

BYOL for Audio This is a demo implementation of BYOL for Audio (BYOL-A), a self-supervised learning method for general-purpose audio representation, includes: Training code that can train models with arbitrary audio files. Evaluation code that can evaluate trained models with downstream tasks. Pretrained weights. If you find BYOL-A useful in your research, please use the following BibTeX entry for citation. @misc{niizumi2021byol-a, title={BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation}, author={Daisuke Niizumi and Daiki Takeuchi and Yasunori Ohishi and Noboru […]

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Machine Learning

TensorFlow implementation of EigenGAN: Layer-Wise Eigen-Learning for GANs

EigenGAN TensorFlow implementation of EigenGAN: Layer-Wise Eigen-Learning for GANs EigenGAN Usage Environment Python 3.6 TensorFlow 1.15 OpenCV, scikit-image, tqdm, oyaml we recommend Anaconda or Miniconda, then you can create the environment with commands below conda create -n EigenGAN python=3.6 source activate EigenGAN conda install opencv scikit-image tqdm tensorflow-gpu=1.15 conda install -c conda-forge oyaml NOTICE: if you create a new conda environment, remember to activate it before any other command source activate EigenGAN Data Preparation CelebA-unaligned (10.2GB, higher quality than the […]

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Machine Learning

Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video

TCMR_RELEASE This repository is the official Pytorch implementation of Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video. The base codes are largely borrowed from VIBE. Qualtitative result Paper teaser video Installation TCMR is tested on Ubuntu 16.04 with Pytorch 1.4 and Python 3.7.10. You may need sudo privilege for the installation. source scripts/install_pip.sh Quick demo Download the pre-trained demo TCMR and required data by below command and download SMPL layers from here (male&female) and […]

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Machine Learning

A lightweight engine for slicing a PyTorch tensor into parallel shards

TorchShard TorchShard is a lightweight engine for slicing a PyTorch tensor into parallel shards. It can reduce GPU memory and scale up the training when the model has massive linear layers (e.g., ViT, BERT and GPT) or huge classes (millions). It has the same API design as PyTorch. Installation pip install torchshard More options in INSTALL.md. Usage import torchshard as ts ts.init_process_group(group_size=2) # init parallel groups m = torch.nn.Sequential( torch.nn.Linear(20, 30, bias=True), ts.nn.ParallelLinear(30, 30, bias=True, dim=None), # equal to nn.Linear() […]

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