I was having trouble using suspend, hibernate and hybrid sleep functionality on Ubuntu 17.04 using Gnome 3.24. The troubles started immediately after installing the uswsusp package:

$ apt-get install uswsusp

I was hopeful and typed systemctl hibernate, but the system got stuck on “Snapshotting system”.

A Turing machine is a mathematical model of a mechanical machine. At its roots, the Turing machine uses a read/write head to manipulate symbols on a tape. It was invented by the computer scientist Alan Turing in 1936. Interestingly, according to the Church-Turing thesis this simple machine can do everything any other computer can do; including our contemporary computers. Though these machines are not a practical or efficient means to calculate something in the real world, they can be used to reason about computability and other properties of computer programs.

The view that machines cannot give rise to surprises is due, I believe, to a fallacy to which philosophers and mathematicians are particularly subject. This is the assumption that as soon as a fact is presented to a mind all consequences of that fact spring into the mind simultaneously with it. It is a very useful assumption under many circumstances, but one too easily forgets that it is false.

– Alan Turing, 1950

Most likely you will have heard that you should never go data fishing, meaning that you should not repeatedly test data. In the case of statistical significance tests, perhaps you will have heard that because of the nature of these tests you will find an effect at the 5% significance level in 5% of cases when there actually is no effect, and an effect at the 2% significance level in 2% of cases when there actually is no effect, and so on. It is less likely you will have heard not to continue looking for an effect after your current test concluded there was none. Here is why.

A little while ago I found myself needing to plot a heat map table in MATLAB. Such a plot is a table where the cells have background colors; the colors depend on the value in the cell, e.g. a higher value could correspond with a warmer color. I found no existing function to do this easily, so I set out to create my own solution.

The code to plot a heat map table can be found here.

Finding models that predict or explain relationships in data is a big focus in information science. Such models often have many parameters that can be tuned, and in practice we only have limited data to tune the parameters with. If we make measurements of a function f(x) at different values of x, we might find data like in Figure (a) below. If we now fit a polynomial curve to all known data points, we might find the model that is depicted in Figure (b). This model appears to explain the data perfectly: all data points are covered. However, such a model does not give any additional insight into the relationship between x and f(x). Indeed; if we make more measurements, we find the data in Figure (c). Now the model we found in (b) appears to not fit the data well at all. In fact, the function used to generate the data is f(x) = x + \epsilon with \epsilon Gaussian noise. The linear model f'(x) = x depicted in Figure (d) is the most suitable model to explain the found data and to make predictions of future data.