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Vijay Mahantesh SM's Projects

arbor icon arbor

a graph visualization library using web workers and jQuery

buttons icon buttons

A CSS button library built using Sass and Compass

courses icon courses

Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

ffmpeg icon ffmpeg

mirror of git://git.videolan.org/ffmpeg.git

foxslide icon foxslide

a jekyll/octopress theme which incorporates bootstrap, html5boilerplate, SEO practices, and a strong hero.

gitbook icon gitbook

Command line utility for generating books and exercises using GitHub/Git and Markdown

human-navigation-algorithm icon human-navigation-algorithm

Human navigation has been a topic of interest in spatial cognition from the past few decades. It has been experimentally observed that humans accomplish the task of way-finding a destination in an unknown environment by recognizing landmarks. Investigations using network analytic techniques reveal that humans, when asked to way-find their destination, learn the top ranked nodes of a network. In this paper we report a study simulating the strategy used by humans to recognize the centers of a network. We show that the paths obtained from our simulation has the same properties as the paths obtained in human based experiment. The simulation thus performed leads to a novel way of path-finding in a network. We discuss the performance of our method and compare it with the existing techniques to find a path between a pair of nodes in a network

k-walk-dominating-set icon k-walk-dominating-set

A dominating set for a graph G = (V, E) is a subset D of V, where the set can be reached from any other vertices in less than or equal k steps with a probability involved is greater than half.

letsencrypt icon letsencrypt

An ACME client that can obtain certs and extensibly update server configurations (currently supports Apache on .deb based systems, nginx support coming soon)

lightgbm icon lightgbm

LightGBM is a fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

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