How to construct a Language: Part 1

I am currently taking MIT’s ConLang class. Over the course of this semester, I’ll construct a fictional language called Nysaerith (IPA: /njsa:eriθ/). I think the best way to improve upon a project like this is to get a ton of feedback. So, I’ll post periodical updates on my progress. Let’s get started!

No language can exist without a fictional world. So, I’ll set the background first. In a fantasy universe, Nysaerith is the language of the magic itself. It binds the world and guides it. Words in Nysaerith have real magical power. Those who speak

become one with the world, and the world bend to their will. They are called mages. There were many mages in the early days. They could move mountains, call upon storms. However, soon their desire for power corrupted them. They fought and massacred each other until no-one who truly understood Nysaerith was left alive.

Many years have passed since then. Now Nysaerith is only preserved in ancient runes and in the incantations that were passed down orally. As the language evolved, its magical power also waned significantly. If this was a fantasy novel, the main character would travel around the world to reconstruct Nysaerinth haha.

Anyways, I think this background is sufficient.

Phonemes

Here is the phonemes of Nysaerinth and also a list of twenty words. You’ll notice that there are several uncommon consonants and long vowels. This is intended because I want this language to sound mystical and rhythmic. Later I’ll write many spells in it.

Image may contain: ‎text that says '‎Nysaerith: A Constructed Language Consonants Plosive Bilabial Fricative Nasal /p/ b/b/ m/ Approximant Labiodental Interdental Trill f/f/ Alveolar t/t/ d/d/ th/8/ s/s/ z/z/ n/n/ Alveopalatal 1// r/r/ rh/rl sh/S/ 13 Palatal Velar c/c/ k/k/ gg kh/x/ gh/y/ y/j/ Uvular Glottal h/h/ Vowels Front Central High Back Mid i/i/ i/i:/ e/e/ e/e:/ ae/ u/u/ u/u:/ ס/၁/ /0/ס Low Diphthongs a/a/ a/a:/ Any two consecutive short vowels result diphthong‎'‎

Some Words

I am continuously improving the language, hence some discrepancy in the future posts is expected. That being said,  any criticism of my word list is welcome!

A Probabilistic Model for COVID-19 Outbreak

As I am writing this blogpost, MIT is about to close until next fall and the undergrads are essentially getting evicted from the dorms.

Last two days were some of the most stressful days I have ever had. I have tons of work to do, but I cannot concentrate because I need to make sure my visa status will be okay, I need to pack my belongings and hopefully I’ll find a place to go.

COVID-19 is spreading rapidly across the US. This was expected. While MIT’s take on this is understandable, was it really necessary?

Let me try to answer that.

Let’s start with a small campus with \(N\) students where a small number of students \(\eta\) have been tested COVID-19 positive. I’ll also assume that the campus is in a state of lockdown — no student leaves the campus and no person from outside enters the campus.
Continue reading A Probabilistic Model for COVID-19 Outbreak

An Introduction to Variational Autoencoders

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These people don’t exist! (source)

Variational Autoencoders (VAE) are really cool machine learning models that can generate new data. It means a VAE trained on thousands of human faces can new human faces as shown above!

Recently, two types of generative models have been popular in the machine learning community, namely, Generative Adversarial Networks (GAN) and VAEs. While GANs have had more success so far, a recent Deepmind paper showed that VAEs can yield results competitive to state of the art GAN models. Furthermore, VAE generated images retain more of the diversity of training dataset than GAN counterparts. Continue reading An Introduction to Variational Autoencoders

The Maths Behind Logistic Regression

I got it wrong 🙁

What are Your Odds?

Let’s have a look at this god-tier math puzzle. Only one out of seven gets it right, and the other six don’t. So, what are the odds for solving it correctly? 1 to 6. Generally, if \(p\) is the probability that someone will get it right, then his/her odds are \(p/(1-p)\).

However, it isn’t necessarily true that \(p=1/7\) for every person because some people are smarter, some have better education and so on. Hence, \(p\) also depends on the person attempting the puzzle. In a Bayesian framework, we capture this dependence with conditional probability.

Continue reading The Maths Behind Logistic Regression

Want to Fight Climate Change? Don’t Waste Food

source: http://res.publicdomainfiles.com/pdf_view/152/13985772022293.jpg

A few days ago, my roommate and I were getting dinner at an Japanese restaurant. While we waited for food, we were having a brief discussion about the recent heat waves in Europe. Both of us felt very sad about these changes. Continue reading Want to Fight Climate Change? Don’t Waste Food

A Week of Poetry: Day 3

Sonnet 130

By William Shakespeare

My mistress’ eyes are nothing like the sun;
Coral is far more red than her lips’ red;
If snow be white, why then her breasts are dun;
If hairs be wires, black wires grow on her head.
I have seen roses damask’d, red and white,
But no such roses see I in her cheeks; Continue reading A Week of Poetry: Day 3

An Online Algorithm to Check for Bipartite Graphs

Bipartite Graphs

A graph \(G(V, E)\) is called bipartite if its vertices can be divided into two groups \(X\) and \(Y\) such that every edge connects one vertex from \(X\) and one vertex from \(Y\). The graph drawn below is bipartite.

Given a graph, can you determine if it is bipartite? Continue reading An Online Algorithm to Check for Bipartite Graphs