The Enigma Machine
> The Enigma Machine was one of the centerpoints of World War II, and its cryptanalysis was one of the stepping stones from breaking codes as an art to cryptography as a science. The machine encrypted messages sent between parts of the German army – operators would type a key on its keyboard, the machine would scramble that, and a letter would light up on the top.
> This notebook simulates an Enigma Machine and visualizes how it works. The Enigma Machine is an especially neat thing to visualize because it was electromechanical. As you used it, it moved. Instead of circuit traces, it had beautiful real wires connecting its pieces.
An introduction to D3.js
> So, you want to create amazing data visualizations on the web and you keep hearing about D3.js. But what is D3.js, and how can you learn it? Let’s start with the question: What is D3? While it might seem like D3.js is an all-encompassing framework, it’s really just a collection of small modules.
The scramble to secure America’s voting machines
> Paperless voting devices are a gaping weakness in the patchwork U.S. election system, security experts say. But among these 14 states and their counties, efforts to replace these machines are slow and uneven, a POLITICO survey reveals.
Very annoying scroll interaction at the top, but eventually some content appears.
> A whole mangrove forest, lighting up all at once, plunging into darkness, then lighting up all again – in near-perfect synchrony. How do thousands of fireflies coordinate with each other? Who is the conductor of this silent symphony?
The Atlas of Moons
> Our solar system collectively hosts nearly 200 known moons, some of which are vibrant worlds in their own right. Take a tour of the major moons in our celestial menagerie, including those that are among the most mystifying—or scientifically intriguing—places in our local neighborhood.
Pretty heavy web page.
Movie plots, visualized.
Urbano Monte’s Massive Map of the Earth (1587)
> In 1587, Urbano Monte made the largest known early map of Earth. The map consists of 60 panels that were meant to be assembled into a planisphere (a circular map that rotates about a central axis) measuring 10 feet across. The David Rumsey Map Center recently acquired a manuscript of Monte’s map and digitally assembled all 60 pieces into the full map (inlined above but click through to zoom/pan).
The Marvelous Mississippi River Meander Maps
> Fisk’s maps represent the memory of a mighty river, with thousands of years of course changes compressed into a single image by a clever mapmaker with an artistic eye. Looking at them, you’re invited to imagine the Mississippi as it was during the European exploration of the Americas in the 1500s, during the Cahokia civilization in the 1200s (when this city’s population matched London’s), when the first humans came upon the river more than 12,000 years ago, and even back to before humans, when mammoths, camels, dire wolves, and giant beavers roamed the land and gazed upon the river.
This map shows the most commonly spoken language in every US state, excluding English and Spanish
> English is, unsurprisingly, the most commonly spoken language across the US, and Spanish is second most common in 46 states and the District of Columbia. So we excluded those two languages in the above map.
Weight Agnostic Neural Networks
> Not all neural network architectures are created equal, some perform much better than others for certain tasks. But how important are the weight parameters of a neural network compared to its architecture? In this work, we question to what extent neural network architectures alone, without learning any weight parameters, can encode solutions for a given task. We propose a search method for neural network architectures that can already perform a task without any explicit weight training. To evaluate these networks, we populate the connections with a single shared weight parameter sampled from a uniform random distribution, and measure the expected performance. We demonstrate that our method can find minimal neural network architectures that can perform several reinforcement learning tasks without weight training. On supervised learning domain, we find architectures that can achieve much higher than chance accuracy on MNIST using random weights.
Some fun demos.
507 Mechanical Movements
> This is an online edition of the classic technical reference Five Hundred and Seven Mechanical Movements by Henry T. Brown.
> This site contains the original illustrations and text from the 21st edition of the book, published in 1908. It also includes animated versions of the illustrations, and occasional notes by the webmaster.
Motorbikes in Taiwan. 3:27.
> Networks rule our world. From the chemical reaction pathways inside a cell, to the web of relationships in an ecosystem, to the trade and political networks that shape the course of history. Or consider this very post you’re reading. You probably found it on a social network, downloaded it from a computer network, and are currently deciphering it with your neural network. But as much as I’ve thought about networks over the years, I didn’t appreciate (until very recently) the importance of simple diffusion. This is our topic for today: the way things move and spread, somewhat chaotically, across a network.
Here Grows New York
> Here Grows New York visually animates the development of this city’s street grid and infrastructure systems from 1609 to the present day, using geo-referenced road network data, historic maps, and geological surveys. The resulting short film presents a series of “cartographic snapshots” of the built-up area at intervals of every 20-30 years in the city’s history. This process highlights the organic spurts of growth and movement that typify New York’s and most cities’ development through time. The result is an abstract representation of urbanism.
Unraveling the JPEG
> JPEG images are everywhere in our digital lives, but behind the veil of familiarity lie algorithms that remove details that are imperceptible to the human eye. This produces the highest visual quality with the smallest file size—but what does that look like? Let’s see what our eyes can’t see!
> This article is about how to decode a JPEG image. In other words, it’s about what it takes to convert the compressed data stored on your computer to the image that appears on the screen. It’s worth learning about not just because it’s important to understand the technology we all use everyday, but also because, as we unravel the layers of compression, we learn a bit about perception and vision, and about what details our eyes are most sensitive to. It’s also just a lot of fun to play with images this way.
Tracking Phones, Google Is a Dragnet for the Police
> The new orders, sometimes called “geofence” warrants, specify an area and a time period, and Google gathers information from Sensorvault about the devices that were there. It labels them with anonymous ID numbers, and detectives look at locations and movement patterns to see if any appear relevant to the crime. Once they narrow the field to a few devices they think belong to suspects or witnesses, Google reveals the users’ names and other information.
Why are my Go executable files so large?
> I built some tooling to extract details about the contents of a Go executable file, and a small D3 application to visualize this information interactively as zoomable tree maps.
Chicago Parking Ticket Visualization
> In this post, I want to show off a fun little web app I made for visualizing parking tickets in Chicago, but because I’ve spent so much time on the overall project, I figured I’d share the story that got me to this point. In many ways, this work is the foundation for my interest in public records and transparency, so it has a very special place in my heart.
Mistakes, we’ve drawn a few
> At The Economist, we take data visualisation seriously. Every week we publish around 40 charts across print, the website and our apps. With every single one, we try our best to visualise the numbers accurately and in a way that best supports the story. But sometimes we get it wrong. We can do better in future if we learn from our mistakes — and other people may be able to learn from them, too.
A great gallery of good and bad graphs.
How to generate uniformly random points on n-spheres and n-balls
> For many Monte Carlo methods, such as those in graphical computing, it is critical to uniformly sample from $d$-dimensional spheres and balls. This post describes over twenty different methods to uniformly random sample from the (surface of) a $d$-dimensional sphere or the (interior of) a $d$-dimensional ball.