New Decimal Systems - Great Sandbox for Data Scientists and Mathematicians

We illustrate pattern recognition techniques applied to an interesting mathematical problem: The representation of a number in non-conventional systems, generalizing the familiar base-2 or base-10 systems. The emphasis is on data science rather than mathematical theory, and the style is that of a tutorial, requiring minimum knowledge in mathematics or statistics. However, some off-the-beaten-path, state-of-the-art number theory research is discussed here, in a way that is accessible to college students after a first course in statistics. This article is also peppered with mathematical and statistical oddities, for instance the fact that there are units of information smaller than the bit.

You will also learn how the discovery process works, as I have included research that I thought would lead me to interesting results, but did not. In all scientific research, only final, successful results are presented, while actually most of the research leads to dead-ends, and is not made available to the reader. Here is your chance to discover these hidden steps, and my thought process!

The topic discussed is one of active research with applications to Blockchain or strong encryption. It is of interest to agencies such as the NSA or private research labs working on security issues, which explains why it is not easy to find many references; some are probably classified documents. As far as I know, it is not part of any university curriculum either. Indeed, the fear of reversibility (successful attacks on cryptographic keys using modern computer networks, new reverse-engineering algorithms, and distributed architecture) has led to the development of quantum algorithms and quantum computers, as well as Blockchain. 

All the results in this article were obtained without writing a single line of code, and replicable as I share my Excel spreadsheets. 

Content of the article
1. General Framework

• Components of number representation systems
• General properties of these systems
• Examples of number representation systems
• Examples of patterns in digit distribution
• Purpose of this research

2. Defects found in the Logistic Map system

3. First step in designing a new system

• First version of new number representation system
• Holes, autocorrelations, and entropy (information theory)

4. Towards a more general, better, hybrid system

• Faulty digits, ergodicity, and high precision computing
• Finding the equilibrium distribution with the percentile test
• Central limit theorem, random walks, Brownian motions
• Data set and Excel computations

5. Related articles

You can read the full article here. 

Two Beautiful Mathematical Results - Part 2

In Part 1 of this article (see here) we featured the two results below, as well as a simple way to prove these formulas.

Here, we continue on the same topic, featuring and proving the formulas below, which are just the tip of the iceberg.

However cool these formulas might look, the biggest contribution here is a general framework to solve much more general problems of this type. The mathematical level is still relatively simple, accessible to people in their first year of college education, if they attended a solid course on calculus. These results are still easy to prove for people who have been exposed to the basics of complex number theory.

I haven't seen these results published anywhere, but my guess is that they are not new. I encourage readers to post questions on Quora or Stackexchange to find more references on this topic, as Google search is of no use here.The focus here is to get more people interested in mathematics, by featuring fascinating results that are not that hard to prove, even for high school students participating in mathematical Olympiads. Also, it could be an interesting, fresh, original topic for university professors to discuss in their lectures or for exams. Finally, if you have seen many similar formulas on Wikipedia (or elsewhere), and you are wondering how they are derived, you will find the solution in this article.

The last section introduces a new, tough, unrelated problem, still unsolved today, that will be of interest to people with a background in probability and/or number theory.

Read the full article.

I Analyzed 10 MM digits of SQRT(2) - Look at My Findings

This article is intended for practitioners who might not necessarily be statisticians or statistically-savvy. The mathematical level is kept as simple as possible, yet I present an original, simple approach to test for randomness, with an interesting application to illustrate the methodology. This material is not something usually discussed in textbooks or classrooms (even for statistical students), offering a fresh perspective, and out-of-the-box tools that are useful in many contexts, as an addition or alternative to traditional tests that are widely used. This article is written as a tutorial, but it also features an interesting research result in the last section. The example used in this tutorial shows how intuiting can be wrong, and why you need data science.

The main question that we want to answer is: Are some events occurring randomly, or is there a mechanism making the events not occurring randomly? What is the gap distribution between two successive events of the same type? In a time-continuous setting (Poisson process) the distribution in question is modeled by the exponential distribution. In the discrete case investigated here, the discrete Poisson process turns out to be a Markov chain, and we are dealing with geometric, rather than exponential distributions. Let us illustrate this with an example.

Example

The digits of the square root of two (SQRT(2)), are believed to be distributed as if they were occurring randomly. Each of the 10 digits 0, 1, ... , 9 appears with a frequency of 10% based on observations, and at any position in the decimal expansion of SQRT(2), on average the next digit does not seem to depend on the value of the previous digit (in short, its value is unpredictable.)  An event in this context is defined, for example, as a digit being equal to (say) 3. The next event is the first time when we find a subsequent digit also equal to 3. The gap (or time elapsed) between two occurrences of the same digit is the main metric that we are interested in, and it is denoted as G. If the digits were distributed just like random numbers, the distribution of the gap G between two occurrences of the same digit, would be geometric

Do you see any pattern in the digits below? Read full article here to find the answer, and to learn more about a powerful statistical technique.