This lab focuses on image transformation techniques in OpenCV with Python. Tasks include creating mirror images using both Affine and Projective transformations, applying a Log Transformer for contrast adjustment, and implementing a Power-Law Transformer for gamma correction.

Modified gap statistic (gap-com) for regularization selection of sparse networks. This method is aimed for complex network estimation.

This the code of my bachelor thesis with the CG2 lab in the university of Crete.

Demonstrate Zipf's law with Sherlock Holmes' stories

Research project that focuses on observing the Resilience of Delhi Road Networks to Traffic Disruptions

South-California earthquakes data analysis.

限られた粒径のマイクロプラスチックの実測データに当てはめたべき分布のスケールパラメタ等から、未計測の粒径範囲を含むマイクロプラスチック濃度を推定するための補正係数の計算方法

Solves the Cauchy Momentum equations for a non-newtonian incompressible fluid using the Finite Difference Method on a 2D grid. The solver is of the explicit type with each iteration corresponding to a step forward in time. The viscosity is updated at each grid point as per the truncated power law.

Create an array containing pseudorandom numbers drawn from a Pareto (Type I) distribution.

Implementation of Power Law Graph Transformer for Machine Translation and Representation Learning.

A DSL for asserting password composition policy effectiveness.

Code and data repository supporting "Power-law scaling relationships link canopy structural complexity and height across forest types."

The project for the course "Web Information Retrieval"

Natural logarithm of the probability density function (PDF) for a Pareto (Type I) distribution.

Create an iterator for generating pseudorandom numbers drawn from a Pareto (Type I) distribution.

Pareto distribution (Type I) probability density function (PDF).

Pareto (Type I) distributed pseudorandom numbers.

Create a readable stream for generating pseudorandom numbers drawn from a Pareto (Type I) distribution.

This repository provides MATLAB implementations of plfit and plpva functions for fitting power-law distributions to empirical data using maximum likelihood estimation (MLE) and statistical goodness-of-fit tests. These tools accurately model complex systems with significant tail behaviors, common in fields like physics, biology, and economics.

Source code for the graph reordering technique, DBG, published in [Faldu et al., IISWC'19].