Quickly identify what's slow with WordPress

A Simple Traffic Generator for Hyperledger Fabric

A check-in system that uses QR codes and email notifications to track attendance at events. It includes a backend server built with express, a data processing script for generating QR codes and IDs, and a frontend scanner built with react and react-scan-qr. The system sends emails using nodemailer and limits the rate of sending with bottleneck.

⭐⭐⭐ Pytorch implementation of Attentiom, Backbone, ViT, MLP, Re-parameter, Convolution, very flexible module combination.

A stopwatch extension for phpunit. Get timing for parts of your code to detect performance bottlenecks.

中国版技术指标

Zabbix Graphs Bottleneck Classification automates bottleneck analysis in network infrastructure using deep learning and the Zabbix monitoring system. It quickly identifies and classifies bottlenecks, enabling proactive network management and optimization.

This project consists of C++ implementations of a 3D Rapidly Exploring Random Tree and three other extensions called RRT*, Execution Extended RRT and Synchronised Greedy Biased RRT. It also includes a heuristically guided RRT* with biased sampling towards relevant bottleneck points predicted by a 3D CNN(modified VoxNet in Tensorflow).

Autoencoders are mostly used for different purposes such as denoising, compression data, anomaly detection, generating new data from the input data entering to the model, and more. This repository introduces a simple autoencoder architecture with some brief explanations of encoder, bottleneck and decoder parts.

Method to estimate the age and intensity of recent bottlenecks/founder events, using genotype data and a recombination map.

Moore Machine Networks (MMN): Learning Finite-State Representations of Recurrent Policy Networks

This is the Algorithm to detect the Handwritten Digits - Autoencoders

A binary+library to measure how much time is spent reading vs writing.

SKYProfiler is a performance monitoring tool for Integration Server. SKYProfiler tracks the service invocations and the monitored data can be seen in real time. This helps users track the time each service invocation takes and further drills down to the child service to identify which service contributes to time.

Tensorflow implementation of deep variational information bottleneck

Python-based code for estimation of highway bottleneck probability using speed transition matrices.

Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"

[This project was completed in September 2020] The GML-Net is a convolutional neural network (CNN) that is based on U-Net architecture with an encoder derived from the ResNet family and BottleNeck blocks that provide reading and aggregation of feature maps from a cross-section of various scales. Effective network learning is ensured by loss func…

A sparse bottleneck neural network to predict electrophysiological properties of neurons from their gene expression.

3 part project: A. bottleneck autoencoder, B. manhattan distance, C. earth mover's distance