• # Creating Cellular Automata: Life-like Cellular Automata

01 Jun 2014

In a continuation of understanding models of life, one of the most interesting cellular automatons is a two dimensional "life-like" automaton. The first life-like automaton was created by John Conway in 1970, and was published in the October 1970 release of Scientific American. The intrigue that surrounds the automaton comes from the emergence and self-organization of highly complex patterns as the simulation evolves. As a result, these automata have attracted the interest of computer scientists, mathematicians, biologists, and physicists.

Figure 1: An example of life-like cellular automata. This simulation is represented as Unicode characters from an automaton implemented in python. White characters represent cells that are alive. As the simulation progresses, it approaches a state of stability and order. Some patches of complexity remain and migrate through the world.

Let's examine the rules of Conway's cellular automaton and see if we can implement a simple life-like cellular automaton in python.

• # Creating Cellular Automata: Elementary Cellular Automata

03 May 2014

Sometimes the best way to learn about something is to create it. If I want to learn about an interesting subset of mathematics, eigenvalues perhaps, the explanation of the math can only go so far. I must let my pencil do the talking as I learn through construction. Programming is no different. So, when I became interested in cellular automata, I decided to make some examples of different types. Today, we can go over elementary automata.

Figure 1: An example of elementary cellular automata following rule 110. Each row in the image represents a segment of time in a time series from top to bottom. Each back space represent an organism, and each white space represents a dead organism. By defining a rule set, we can determine the outcome of the system. Rule 110 is named as the binary equivalent to the binary series 01101110.

Elementary automata are particularly interesting for two reasons:

1. The limited number of rules and interactions make them very easy to study.
2. The visual nature of time allows for deep investigation into changing patterns.

With these two characteristics, elementary cellular automata have become a tool to explore emergence, chaos, and complexity in a non-linear system.

• # Connecting to Cisco IPSec VPNs on Arch Linux

26 Feb 2014

In preparation to some travel abroad to Ivrea, Italy, I decided that I needed a secure way to connect back to my server at college. SUNY Geneseo is kind enough to provide a Cisco IPSec VPN into their heavily firewalled network and, with a little work, we can VPN in without an issue.

Figure 1: A generic visualization of the priciples of VPN tunneling. In this figure distict networks are able to connect with one another via the internet whilst preserving annonymity. Image by Ludovic.ferre licensed under Creative Commons Attribution-Share Alike 3.0 Unported.

## VPNC and the OpenConnect Client

Cisco provides a proprietary VPN client for users, however this application lacks official linux support, and remains unstable on Arch Linux. The open source community has created an alternative to the Cisco VPN client called the OpenConnect Client. Arch Linux has a package in the official repositories called openconnect To install, open a terminal and run

pacman -S openconnect


Once installed, we can configure and initialize a VPN instance using the openconnect command.

• # A Case for Public Data in Higher Education

23 Jan 2014

It has become commonplace for today's universities to release faculty evaluations to their students as a way to assist students in choosing classes. Recently, however, Yale decided that public access to this freely available information should not be allowed when they blocked university access to Yale Bluebook+, a student-made service to view faculty evaluations along with course registration information.[1][2] At my alma mater, SUNY Geneseo, our faculty evaluation are obscured from view as non-searchable pdf files never to be seen by students again. As shown in Figure 1, this approach has consequences.[3][4]

Figure 1: The decline of average SOFI response by semester. This chart shows a clear and startling decline of almost 40% in the last four years. I hypothesize that the response percentage for SOFIs is decreasing because the data collected has no impact on students in its current form.

When students are unable to see the results of their evaluations, participation drops. When participation drops, the data become less valuable and the cycle continues. On the flip side, if the data were to become useful again -- as a metric for students to choose future professors, perhaps -- we could restore the worth of the SOFI data. My question is, what would happen if this data were to be place directly in the hands of the people who need it the most, exactly when they need it?