Why are we somtimes able to recognize missplled wrods and simahimes not? Come to be a subject in our study to help figure out what is going on in the brain when we read spelling mistakes! What you will be doing We are investigating how semantic representations in the brain evolve as a function of the degree of word distortion. The study is mostly performed inside an magnetoencephalography (MEG) scanner, which (passively) measures the magnetic field generated by neurons in your brain (about 90 mins). During the MEG measurement, you will be presented with a series of visual stimuli (words and misspelled pseudowords) and you are asked to try and figure out what they mean. We will also want a magnetic resonance image (MRI) of your head, which we will do in a separate session (about 30 mins).

LaTeX is the best tool for writing scientific papers. Microsoft Word is the best tool for commenting on manuscripts. Hence, ideally, you would write your paper in LaTeX, and send a .docx version to your co-authors for comments. Pandoc is a tool for converting text between different markup languages. Impressive as its capabilities are, there are some markup languages, such as LaTeX, that are essentially programming languages, and Pandoc cannot hope to cover all its commands, extensions, and so on. In my experience, plain Pandoc will get you 90% of the way. In this document, I'll describe how to get the final 10%. Example paper and code

Goal To have a comfortable way to run analyses on the workstation in your office from your laptop that may be anywhere in the world. While running terminal programs is best done through plain SSH, in this tutorial we will be looking at using VNC to create a full-fledged remote desktop. Setup on your Aalto Ubuntu workstation Install TigerVNC Install TigerVNC on your workstation by running in a terminal: pkcon install tigervnc-standalone-server. You'll need to be the designated primary user of the workstation in order to be allowed to install packages (contact IT if you don't have this permission and feel you should have). Setup a password

The term "chain of trust" comes from the domain of computer security, where it is one of the core principles behind verifying digital signatures. To know whether a signature is valid, a list of endorsing signatures is added to the original signature. These endorsing signatures can in turn have endorsing signatures themselves to help verify them, and so forth. When one or more signatures along this chain comes from an entity you explicitly trust (a family member, friend or trustworthy institution), the original signature can be trust implicitly. This document outlines an experiment to see what it would take to create a chain of trust for sentences in a text article. The idea is to track down articles that support the claim made in the sentence, then track down articles that support the supporting articles, and so forth, until articles from explicitly trusted sources are reached (mainly peer reviewed journal articles). Moreover, we want to visualize this chain of trust so we get an overview of the origins of a claim, including which articles support and refute it, and how trustworthy those articles are in turn. While with digital signatures, we can deal in absolutes (either a signature endorses another signature or not), with text we will probably have to settle for a score indicating the degree of trust. A simple scenario In academic articles, the idea of a "chain of trust" is explicitly encoded in the usage of citations. Hence, with these sorts of articles it should be considerably easier to get a basic system going to visualize this chain of trust than for example an article in a newspaper. The main difficulty here is that a citation usually does not mention the specific sentence in the article that directly supports the claim.

You don't know what you have until it's gone. As a Dutch citizen living abroad, it has become painfully clear not only how wonderful the Dutch cycling culture really is, but also how misunderstood by the rest of the world. I'm a fan of the YouTube channel "Not Just Bikes" and I think it does a better job than most of getting across the way cycling is integrated into everyday life in the Netherlands. However, as with most videos about bicycles, cars end up playing a large part in the narrative: cycling as an alternative to driving. As a Dutch person for whom the bicycle has been his day-to-day transport since the age of 6, this perspective seems to be missing the mark. For the Dutch, cycling feels like an extension to being a pedestrian. Whatever these guys seem to be doing has nothing to do with the Dutch concept of cycling: These contraptions are not proper bicycles! What are they wearing? Why are they on the road throwing themselves in front of the cars? How could anyone ever think this is a good mode of transportation? This has nothing to do with the Dutch concept of cycling. It is in fact so far off, that I'm going need a new word without any connotations to *waves hands around* whatever this is, if I want to explain the Dutch perspective. Introducing: the velo Let's use the French word "velo", as it blends better with English than the Dutch "fiets". Velo is short for velocity and that is exactly what this machine will do for you: it gives you extra velocity for free. "For free" being the key point here. Unlike the bicycle, where the amplification of speed comes at the cost of wearing special clothing, risking your life dodging cars, having to shower afterwards, spending time taking it out and locking it up, the velo amplifies your speed for free. This requirement drives all aspects of the design and usage of a velo.

This code and an emulator to run it can be found at: https://github.com/wmvanvliet/8bit Slow multiplication In one of his video's, Ben Eater showed us a program to multiply two numbers. In short, to multiply $x$ by $y$, you would add $x$ to the result $y$ times, using a loop: ; ; Multiply two numbers using a loop (Ben Eater's design) ; loop: lda prod

It's surprising how much power a simple computer architecture as the SAP-1 has. This is a report of my journey to create a program that computes the square root of any integer number $0 \leq S \leq 255$. Unfortunately this is impossible in just 16 bytes of RAM, so I will be using a version of Ben Eater's breadboard computer that has had its memory expanded to 256 bytes, following the guide of /u/MironV. However, we will not be using any extra instructions, flags or control lines. The code and an emulator to run it are available here: https://github.com/wmvanvliet/8bit/tree/ext_memory. The Babylonian method for computing the square root To method we will use to compute the square root is known as the Babylonian method. In C, it goes like this:

So you have finished your Ben Eater breadboard computer build? And now you want to learn a little more, add a little more power, and write a bit more ambitious programs? Here are 10 improvements to the original design that are relatively easy to do, require only a few more ICs, and will all fit on the original 2x7 breadboards. I suggest making these improvements in the order they are presented here. The description of each improvement assumes that the previous improvements have been implemented. Origins of these improvements This document got started because I decided to source most of my components from a local electronics shop for my build. This meant the exact ICs that Ben Eater used were not always available and I sometimes had to use different ICs, notably:

Here are some tricks of the Python scientific ecosystem that you may not know about yet. The magic appearing dimension [None] Here is a boring 1D Numpy array: x = np.arange(10) Indexing with None adds empty dimensions on the fly. Need x as column vector? x[:, None] Need x as row vector? x[None, :]