## p@55w0rd\$: Part 1

There are many things we need to function every day: love, family and friends, good health, puppies, Toastmasters. There is one more: passwords. Think about it. We use passwords every day, when we check our computer, phone, email, Facebook, Twitter, bank account—basically, anything that represents us. Passwords are valuable.

## Sierpinski Shirt

This weekend and next, Austin has its annual East Art Studio Tour. I bought this shirt at Art.Science.Gallery. I liked that the shirt was actually mathematical. Art.Science.Gallery is also in danger of closing and is looking to raise funds, so please visit their website to see how you can help.

## Braille in Modern World

You may have seen braille in elevators and on ATMs and door signs, but brushed it off as something that’s for blind people and not for you. As a student, you may have seen braille in a math problem involving patterns and binary choices. As a puzzle enthusiast, you may have seen braille in a decryption challenge. But is that all there is to braille?

For an upcoming Toastmasters speech, I decided to get to know braille, by researching and interviewing locals who professionally work with people who are blind and visually impaired. Surprisingly, the more I looked into braille, the more I realized its diminishing role in the modern world. I want to address the problem today.

## Solving Nonograms with Compressive Sensing: Part 4

Last time, we considered the linear equations and linear inequalities that the solution of a nonogram must satisfy. Let us now solve the minimization problem and see how well compressive sensing works. I will consider several examples, then offer a simple fix for the problems that we will encounter.

## Solving Nonograms with Compressive Sensing: Part 3

We know how to represent the solution of a nonogram as a sparse vector $\vec{x} \in \{0,\,1\}^{N}$. Let us now design the constraints; they will be linear equations and linear inequalities that the entries of $\vec{x}$ must satisfy according to the nonogram. The only information that the nonogram provides are the row and column sequences, so we want to make the most of it. Afterwards, we obtain the sparse vector $\vec{x}$ by minimizing its 1-norm under these constraints. I will discuss the code and the results in the next and final post.