Segm_Ident_Vertebrae_CNN_kmeans_knn Segmentation and Identification of Vertebrae in CT Scans using CNN, k-means Clustering and k-NN If you use this code for your research, please cite our paper: @Article{informatics8020040, AUTHOR = {Altini, Nicola and De Giosa, Giuseppe and Fragasso, Nicola and Coscia, Claudia and Sibilano, Elena and Prencipe, Berardino and Hussain, Sardar Mehboob and Brunetti, Antonio and Buongiorno, Domenico and Guerriero, Andrea and Tatò, Ilaria Sabina and Brunetti, Gioacchino and Triggiani, Vito and Bevilacqua, Vitoantonio}, TITLE = {Segmentation and Identification of […]

This is an unofficial PyTorch implemenation of EventProp, a method to compute exact gradients for Spiking Neural Networks. The repo currently contains code to train a 1-layer Spiking Neural Network with leaky integrate-and-fire (LIF) neurons for 10-way digit classification on MNIST. Implementation Details The implementation of EventProp itself is in models.py, in form of the forward and backward methods of the SpikingLinear module, which compute the forward passes of a spiking layer and its adjoint layer. In particular, the manual_forward […]

CT-Net CT-Net: Channel Tensorization Network for Video Classification @inproceedings{ li2021ctnet, title={{{}CT{}}-Net: Channel Tensorization Network for Video Classification}, author={Kunchang Li and Xianhang Li and Yali Wang and Jun Wang and Yu Qiao}, booktitle={International Conference on Learning Representations}, year={2021}, url={https://openreview.net/forum?id=UoaQUQREMOs} } Overview [2021/6/3] We release the PyTorch code of CT-Net. More details and models will be available. Model Zoo More models will be released in a month… Now we release the model for visualization, please download it from here and put it […]

self-classifier Official PyTorch implementation and pretrained models of the paper Self-Supervised Classification Network. Self-Classifier is a self-supervised end-to-end classification neural network. It learns labels and representations simultaneously in a single-stage end-to-end manner. Self-Classifier architecture. Two augmented views of the same image are processed by a shared network. The cross-entropy of the two views is minimized to promote same class prediction while avoiding degenerate solutions by asserting a uniform prior. The resulting model learns representations and class labels in a single-stage […]

SparseConvNet Submanifold Sparse Convolutional NetworksThis is the PyTorch library for training Submanifold Sparse Convolutional Networks. Spatial sparsity This library brings Spatially-sparse convolutional networks to PyTorch. Moreover, it introduces Submanifold Sparse Convolutions, that can be used to build computationally efficient sparse VGG/ResNet/DenseNet-style networks. With regular 3×3 convolutions, the set of active (non-zero) sites grows rapidly: With Submanifold Sparse Convolutions, the set of active sites is unchanged. Active sites look at their active neighbors (green); non-active sites (red) have no computational overhead: […]

python-can The Controller Area Network is a bus standard designed to allow microcontrollers and devices to communicate with each other. It has priority based bus arbitration and reliable deterministic communication. It is used in cars, trucks, boats, wheelchairs and more. The can package provides controller area network support for Python developers; providing common abstractions to different hardware devices, and a suite of utilities for sending and receiving messages on a can bus. The library currently supports Python 3.6+ as well […]

Surface water network Creates surface water network, which can be used to create MODFLOW’s SFR. Python packages Python 3.6+ is required. Required geopandas – process spatial data similar to pandas pyproj>=2.2 – spatial projection support rtree – spatial index support Optional flopy – read/write MODFLOW models netCDF4 – used to read TopNet files Testing Run pytest -v or python3 -m pytest -v For faster multi-core pytest -v -n 2 (with pytest-xdist) Examples import geopandas import pandas as pd import swn […]

url-shortener-chalice A simple URL shortener app using AWS Chalice. Please make sure your to configure your AWS credentials before starting with deploying things onto AWS. aws configure Dependencies are included in the file: requirements.txt Please note the below chalice scheduler is configured to clean up the dynamo-db table entries every 24 hours. Deployment steps: aws cloudformation deploy –template-file .chalicedynamodb_cf_template.yaml –stack-name “url-shortner-stack” chalice deploy Testing steps screenshots: Teardown steps: chalice delete aws cloudformation delete-stack –stack-name “url-shortner-stack” GitHub https://github.com/rg666/url-shortener-chalice    

Suez Tool for pretty printing and optimizing Lightning Network channels. Installation Install poetry poetry install poetry run ./suez Channel fee policy You can set channel fees by passing –base-fee and –fee-rate parameters. For example: poetry run ./suez –base-fee 1000 –fee-rate 200 You can override the channel fee policy by changing the FeePolicy class. Example implementation does the following: sets lower fee rate for channels with mostly local balance sets higher fee rate for channels with mostly remote balance sets medium […]

DNSStager DNSStager is an open-source project based on Python used to hide and transfer your payload using DNS. DNSStager will create a malicious DNS server that handles DNS requests to your domain and return your payload as a response to specific record requests such as AAAA or TXT records after splitting it into chunks and encoding the payload using different algorithms. DNSStager can generate a custom agent written in C or GoLang that will resolve a sequence of domains, retrieve […]