This blog needs some lightening up and what a better way to do that than post some graphs? Exactly! I like to collect data on various things and then make a graph of them. Things are much better when presented with axis and some number around it.
Not so long time ago I had birthday. Although it happens rather regularly, the last one was special. For the first time I’ve enabled notification on Facebook. It appears that people (a.k.a. friends) are rather nice and are willing to write something positive about you on that day. Here is to memory of that special day!
I’ve scraped Facebook’s posts and messages which were within 48 h from the Birthday 00:01 AM. Sent times are aggregated on the graph and text content is summarised in word stats.
Green graph displays wishes density as a function of time obtained using Gaussian Kernel Distribution Estimation. Blue dots show cumulative wish count. Both are normalised to highest value being 1. Statistically speaking, green and blue shows (up to constants) probability density function and cumulative density function respectively.
What is nice about this graph is that it generally shows at what time of the day my peers are active. It seems that majority is active in the morning 10-11 am and after 8 pm, which is reassuring that they are very normal average people.
Additional insight gives extracted content. Although there’s not enough data, I think it nicely hints on my background. I’ll leave interpretation, but will also point up that the percentage of wish to total falls closely to the ratio of people who read posts, i.e. 12 – 16 % (even though this is not related as birthday notification goes to everyone).
Happy Birthday: 34 (38.20%)
Wszystkiego najlepszego/All the best: 11 (12.36%)
(tylko) Najlepszego/(only) The best: 10 (11.24%)
Sto lat/100+ years: 16 (17.98%)
Smileys: 25 (28.09%)
Kisses: 13 (14.61%)
Hearths: 3 (3.37%)
Dawid: 20 (22.47%)
You: 17 (19.1%)
Boy: 2 (2.23%)
Total number of exclamation mark (!):
tl;dr: I’m going less quality, more quantity.
I like to write. I write lots, but usually don’t publish it. Whenever I think about something and write it down, I then rethink what I thought and want to rewrite what I wrote. It takes me ages to write something very precise and something that I wouldn’t be immediately ashamed of. This blog was meant to be the place for that content, for things I wouldn’t quickly regret. And, although, I’m rather OK with the content, I’m deeply disappointed with the frequency. Thus: change.
This blog initially was meant to be my academical window. Something that when people look me up (for whatever reason), they’d see something over which I would have control. However, my view on World has yet again changed. Despite my passion for research and likeness for academia free-thinking diverse environment, I’ve decided to leave it. There is so much great research being done in industry and private sector that I think I’m shooting myself in foot by sticking only to that audience.
Plan for this blog is changing to present cool things that happen and fun stuff I’m working on. I enjoy working and I like to write. If the content is not up to standard quality, shame. But not having a content at all is just disgraceful. How can I show people what I’m working on, if I don’t show them anything at all.
In Python’s mantra: It’s easier to ask for forgiveness than for permission.
Another of my papers  (see About) has been published recently. Unfortunately, I cannot present it here in a full scope. Below I’m presenting an abstract from the paper and invite interested people in contacting me. This topic is really interesting and there is plenty to be done.
On the Phase Coupling of Two Components Mixing in Empirical Mode Decomposition
This paper investigates frequency mixing effect of Empirical Mode Decomposition (EMD) and explores whether it can be explained by simple phase coupling between components of the input signal. The input is assumed to be a linear combination of harmonic oscillators. The hypothesis was tested assuming that phases of input signals’ components would couple according to Kuramoto’s model. Using a Kuramoto’s model with as many oscillators as the number of intrinsic mode functions (result of EMD), the model’s parameters were adjusted by a particle swarm optimisation (PSO) method. The results show that our hypothesis is plausible, however, a different coupling mechanism than the simple sine-coupling Kuramoto’s model are likely to give better results.
In a very, very brief, but figure-enhanced summary: we present that for certain range of frequencies ratio a mix in frequency appears which can be explained as a difference of original frequencies. Figure below presents Fourier transform F of correlation function between each pair of IMFs for different values of frequency f. All values were normalised, such that for given f the maximum is equal to one. Additionally, on the top projection of the figure two lines have been drawn – F1 = 13-f (dashed line) and F2 = 2(13 – f) (dash-dotted line).
 D. Laszuk, O. J. Cadenas, and S. J. Nasuto (July, 2016) On the Phase Coupling of Two Components Mixing in Empirical Mode Decomposition, Advances in Data Science and Adaptive Analysis, vol. 8, no. 1. [Link]