adenadel

My master's degree. I did a relatively unique (and affordable) program at the University of Oregon that focuses on professional development for scientists and includes a 9 month internship. The program and my internship have completely shaped my professional interests and I think that the program does a really good job of preparing STEM undergrads for careers in industry.

I should have been more clear. This is what I meant be "correct encodings". I would be surprised (but am welcome to being wrong) if a large amount of sheet music hadn't been encoded somehow.

I believe this is what the page is referring to (The Dawning of the Age of Stochasticity)

https://www.dam.brown.edu/people/mumford/beyond/papers/2000b...

Intuitively (to me), it feels like a clever variation of something like Needleman-Wunsch could pull this off without using machine learning (as long as you have the right encodings). Can someone enlighten me?

I am not well-informed on this topic, but isn't men incorporating toxic things into their identity precisely what toxic masculinity is (i.e. to me it sounds like you do believe in toxic masculinity)?

Please correct me if I'm wrong. I want to learn.

San Diego is right in the range that you are describing. Average highs in August are ~75. I'm biased though since I lived in San Diego for a couple of years and have never lived in or near San Francisco. Inland is much hotter in San Diego though.

https://www.weather-us.com/en/california-usa/san-diego-clima...

In case anyone is interested, I ran this procedure using integer bases besides 10 and obtained the following sequence

1, 5, 11, 19, 29, 41, 55, 71, 89, ...

Searching it on the OEIS (a great resource for mathematics) gave

http://oeis.org/search?q=1%2C5%2C+11%2C+19%2C+29%2C+41%2C+55...

It turns out that these are precisely the first values of the Fibonacci polynomial n^2 - n - 1

I haven't verified this fact, but it seems like it comes from an application of the generating function of the Fibonacci numbers.

I posted my code in another comment

https://news.ycombinator.com/item?id=24933085

I think I found a typo in the final line (not impactful for the result, however).

11/89 should be 11/8900

This can be verified by using the following Sage code (simply go to https://sagecell.sagemath.org/ to avoid installing sage)

  b = 10

  A = Matrix([[0, 1],[1/(b**2), 1/b]])
  I = matrix.identity(2)

  show((I - A).inverse() * Matrix([[1/b**2],[1/b**3]]))

You might be interested in the idea of a copula and Sklar's theorem

https://en.wikipedia.org/wiki/Copula_(probability_theory)#Sk...

Well, even if "everyone" knows it, at some point you have to come across it for the first time.

https://xkcd.com/1053/

Hm, I thought this paper out of the Broad came up with a novel method. I haven't actually read it, but I saw a lot of press about it at the time.

https://www.nature.com/articles/s41586-020-2477-4

Can a synthetic biologist comment on whether or not something like mitochondrial base editing should have been included. If it should have, is its exclusion presumably due to the fact that it was published so recently?

I think this is a nice article, but I can't help but be frustrated by the stochasticity of HN since I posted it yesterday :D

You definitely have heard of some of their investors :)

I think these are great points. I justify the lack of some of the fundamental concepts like Gaussian elimination to be due to the fact that the book is meant for a second course in linear algebra. My copy has dual and quotient spaces and I think these are important topics to get an introduction to if you're looking to learn functional analysis, topology, etc. Some discussion of finite fields could be interesting. This has prompted me to look up linear algebra over finite fields.

I think that this is a great exercise to really learn the implementation details of the methods, but the point of ISLR is more to get scientists (and the like) up and running with the usage of these methods.

I want to share my favorite linear algebra textbook (Linear Algebra Done Right by Axler)

http://linear.axler.net/

It's meant for a second course in linear algebra and the focus is on abstract vector spaces and linear transformations (rather than a table of numbers perspective). It also doesn't use determinants until the last chapter.

Here's the game that the video creator has made based on these ideas

https://store.steampowered.com/app/1256230/Hyperbolica/

Well, characteristic functions are more general than MGFs (in that they are always finite) and have very useful properties, but they have a similar definition and are also based on an expectation.

This may be pedantic, but an object that determines a distribution that isn't based on expected values is the CDF. In my first probability course in undergrad I think we defined probability mass functions and probability density functions first and defined CDFs in terms of them, but from a measure theoretic point of view, the CDF is more fundamental since it is defined for continuous and discrete distributions (and also for distributions that are neither).

I wouldn't say that the existence of the normal distribution is necessarily the reason that we place significance on the mean and variance. The mean is incredibly natural to define when you move to measure theoretic probability (simply the integral of a function, i.e. a random variable, with respect to some measure). When you take this point of view, random variables with particular moments existing are simply functions in L^p. Further, when you move on to proving the CLT the moments give you properties of the characteristic functions that allow you to prove the CLT. These are all deep connections.

There's another interpretation of the mean (and conditional expectation) as quantities minimizing squared error. It's not surprising that squared error and variance are so similar and that these are connected.

I got one with a bitly link to a completely unrelated webpage

Wow, so interesting. I go to school in the northeast. I'll have to ask around.

Has anyone else ever heard of an MD-PhD student being called a "mud-phud"? I'm in a PhD program (and have several friends that are MD-PhD candidates) and my wife is an MD and I've never heard of this term.

All of the residency announcements I saw on Facebook were also wildly happy, but I know for a fact that some of the people who posted those were literally in tears before that and put themselves together to post a "happy" announcement. Personally, I like the idea of having the option to choose whether or not to accept an offer.

I'm not so sold on the idea that a match system will improve labor liquidity. How do you negotiate wages? Medical residents are stuck with the salary of the site that they matched at. When you enter the match, you cannot simply choose not to accept the job "offer" you are given.

From my experience with friends going through the residency match, more people end up unhappy or mediocre about their match rather than happy. I say this as the partner of someone who matched at their number one choice. The day was defined for me by how I felt for all of our friends who didn't end up at their number one (or 2nd-5th choice) and ended up feeling somewhat broken.

Do you still have the assignment?

Something somewhat similar is How to Become a Pure Mathematician (or Statistician)

http://hbpms.blogspot.com/

Read the first sentence of the second paragraph.

Here's a Numberphile video on this

https://www.youtube.com/watch?v=w-I6XTVZXww

This is sort of a spiritual sequel to

Can a biologist fix a radio?--Or, what I learned while studying apoptosis. https://www.ncbi.nlm.nih.gov/pubmed/12242150

For me, The Catcher in the Rye was what led me to his other books. Franny and Zooey was my favorite book for a long time (although it's been nearly a decade since I read it) and I think The Catcher in the Rye is probably the weakest of his four books in my opinion (and very likely the most polarizing).