jethkl

Mixed. The CO2 emission trend in US is trending down, the trend started ~2007 [1]. That's heartening. But I expect the next few decades to be rough.

[1] https://www.macrotrends.net/countries/USA/united-states/carb...

Tens of millions of German marks. They were stashed between the pages of a book, and they came from the era of hyperinflation during the 1920s.

That sounds like a good idea! Perhaps expand what notaries do?

From the article: "The nine dimensional haystack is a model used to quantify the parameter space Earth observers must pick through to rule out the existence of signals originating from extraterrestrial civilizations."

The image at head of page is of the well-known Wow! signal: https://en.wikipedia.org/wiki/Wow!_signal

I was in Penner's class. Hard work, but I think it was worth it.

A version of that exists: https://www.webdesignmuseum.org

In Dec 2012, details of gun permits issued in Westchester, Rockland and Putnam counties just north of NYC were posted on an interactive map [1]. This was a nearly perfect test of the theory that criminal behavior would get worse in response to easy access to this kind of information. I found no news reports indicating people had been targeted one way or the other, and official stats [2] indicate serious crime decreased.

[1] https://www.npr.org/sections/thetwo-way/2012/12/26/168075748...

[2] https://westchesterindex.org/community/serious-crimes

> Matrix multiplication is the composition of linear maps.

This is an insight due to Cayley (see @ogogmad's comment, [1], and [2]).

> If your professor...

My professor provided historical context.

[1] https://abel.math.harvard.edu/~knill/history/matrix/index.ht...

[2] https://ia802808.us.archive.org/0/items/philtrans05474612/05...

I agree with prior comments about the article (not great). But I love LA, and will indulge a bit.

For me, the most amusing feature of LA is how quickly results and tools escalate from trivial and obvious to surprising and hard. LA is often introduced as slick notation to express systems of linear equations. Gaussian elimination and matrix inverses (when they exist) are natural evolutions of this, but not what I consider surprising.

Matrix multiplication is an elementary next step, but it is not at all clear why one would multiply matrices. My first real mathematical surprise (I was in jr high at the time) is a result of matrix multiplication: there is a matrix A of real values that satisfies A @ A = -I. (The matrix is [[0,1],[-1,0]]). I was aware of complex numbers, but until that moment, I didn't know you could get "minus identity" without first artificially introducing the idea of "square root of -1". But this shows one can construct "square root of -1" without introducing anything like that.

When one gets to determinants, characteristic polynomials, eigenstructure, fixed subspaces, projection operators, SVD, orthogonal transformations, and so-on, the work really begins. That determinants are multiplicative is really wonderful, but not at all obvious. SVD in full generality was only shown in 1936 and modern algorithms emerged in the 1950's to compute it [1].

Finally, check out the LDU decomposition of the Walsh matrix [2].

[1] https://en.wikipedia.org/wiki/Singular_value_decomposition#H...

[2] https://en.wikipedia.org/wiki/Walsh_matrix

interesting take, but I don't see any problems here. Conspiratorial hallmarks? I'll try to defend. My comments about ≥80% market etc. are common knowledge and the conclusion is consistent. And my post prior to that is about my observations from a few years ago. Whatever you think of that claim, it is consistent with what is widely known (or easily inferred) about Google: they have top-tier engineering talent that has incentive to get tidy searches sent to them by dynamically generating "is similar to" canonical and trending matches, and aggressively pushing those matches to users before hitting submit. My claim is simply that I noticed Google succeeded in building a better version of this than their competition. I don't see anything suspect there.

The interesting artifact of this is that the dominant set of queries becomes a very small proper subset of natural/organic queries.

There is hint of "what" at end of my previous comment, but no larger assertion is made.

For "what" -- I don't specify. Regarding "how I know", I start with several premises:

1) search engines are the dominant way to find information now.

2) conservatively, ≥80% of searches in US are through google.

3) google is not a passive observer of user search queries, but instead actively suggests queries to users.

It follows that there must be some impact. The "engagement" effect is this: I start a search for Depeche Mode, but get distracted by suggestions for J Depp. In contrast, DDG suggests Depakote, Depression, Dept of Revenue and a few other things. Other search engines seem to be less aggressive.

In prior life, I worked at place where we had access to data that contained user searches. We could plainly see that google searches, due to autocomplete, "quantize" results in a way that bing and others don't. The result is that google searches are almost inorganic crafted things, and they contain unusual artifacts. The impact on society is definitely real.

At the end of the YT video, the audience applause seems tepid, nobody stands, and yet the performers must be exhausted. (I think it's a great performance.) There's no singable theme, nothing really catchy, just 15 minutes of pretty intense focus required by everybody. For me, it's completely gripping - it can't be background music and I cannot do other work if it is playing. I think it is somehow alien, and so it will never be as widely celebrated as his other works.

Late Beethoven works are astonishing. The Great Fugue [1,2] is also remarkable -- fiendishly tough on both audiences and players. It is hard to believe it was written during the early 19th century. The fugue also has an interesting story: originally the 4th movement of a quartet, its terrible reception caused the publisher to request a new 4th movement, and the fugue became a standalone piece. The best description I've heard of it is from Stravinsky: "a contemporary piece of music that will be contemporary forever".

[1] https://en.wikipedia.org/wiki/Grosse_Fuge

[2] https://www.youtube.com/watch?v=13ygvpIg-S0

All I know about the images comes from the original announcement [1]. The gif in the question is toward the bottom. No moon indeed.

[1] https://blogs.nasa.gov/webb/2022/05/09/miris-sharper-view-hi...

It is well-known, but not elite. A small minority of places filter by school, but most do not. They hire the person.

PhD in math, US perspective, 20ish years working.

Members from my class (including +/- a couple of years) went to startups, think tanks, SEC, NSA, trading, hedge funds, digital media, academia, postdocs, consulting, and commercial research labs that are incubators within larger corporations.

You didn't indicate your specialty, but it probably does not matter. Many current DS and related jobs list PhD in a STEM field as baseline or preferred requirement. I know logicians who got non-academic work (this is not a dig at logic, they were really worried about this).

If you want to go the software developer route, at this point you do know how to study for leetcode type puzzles. But you are in direct competition with many others who specialize in that, and a PhD in math will not carry much extra weight in the hiring decision.

Watching my own and my peers careers evolve over time, the impact of one's network should not be underestimated.

Finally, assume you will never be asked about your dissertation. Ever.

That is a great list, though I think content discovery is still a hard issue. Below are a few music-centered channels. To illustrate that discovery is hard, I had a bit of a struggle to find a few of them, even when I knew what I was looking for. Content covers performance, music theory, money, and history. I also verified no overlap with your list.

Adam Neely

NPR Music (Tiny desk)

Paul Davids

Rick Beato

Smalin

It is unclear whether you are asking about integer programs or linear, so I'll address both.

Integer programming is very hard, and is an active area of research. The space of problems that arise in practice typically have sparsity, special structure, and other features that allow for shortcuts to be employed so that brute force isn't needed.

Interior point solvers are guaranteed to find a global solution, and it can be proved that they will do so efficiently [1]. Practical implementations leverage decades of experience to improve upon the theoretical guarantees.

[1] https://en.wikipedia.org/wiki/Convex_optimization

Great to see OR highlighted here! Thank you for sharing.

The syntax demonstrated is very similar to the syntax of other libraries, including the python tools provided by Gurobi [1].

For experimentation and rapid prototyping LP, QP, MIP and geometric programming problems, I highly recommend CVXPY [2]. CVXPY allows the user to select a solver. Among the solvers is GLOP [3].

[1] https://www.gurobi.com/resource/modeling-examples-using-the-...

[2] http://cvxpy.org

[3] https://www.cvxpy.org/tutorial/advanced/index.html#choosing-...

This topic also came up a few weeks ago. I speculate there is no upper bound. A similar example using "Hawking": https://news.ycombinator.com/item?id=30626497#30653029

This is distant past for me, but I'll clarify two things: everyone that I encountered seemed quite sharp - it was a strong and interesting group. Next, my experience was personally unpleasant and parts of it still stick with me, but it was not horror-level. I also recognize it's a gigantic company, and chalk my day up to a bad match and statistical flukes.

When I left the building that day, due to the late start and shifted schedule, the office was basically empty. I made it a few blocks on my walk back to the hotel when a thunderstorm rolled in. Lacking a phone and the option to return to the building to find a person, I ran. Didn't make it. I actually had forgotten about this part until I reflected back on it this morning. It was a fitting end.

> weird or obscure tests.

I'm a bit relieved to hear this: one of the weirdest professional experiences I have ever had was at an AWS interview. I've often wondered which parts of that day were a psych test or simply human error.

Timing of the interview was shortly before the HQ2 announcement (2017 I think), and the interview was in the outskirts of DC. I was told specifically not to bring a phone, which I thought was odd, but I didn't think I'd need one for the day since I'd be on site. So, per the instructions, I left it in the hotel, walked to the site, and checked in at the front desk. Unfortunately, they texted me 15 minutes before the start time that the schedule had shifted 2h later. The front desk did not get the message. So I sat in the lobby, repeatedly checked in every so often, had polite conversations with a random assortment of people, drank some coffee, and waited. That was the best part of the day. Each part of the remainder of the actual interview process had a similar "this seems a bit off" feature.

Also Oprah Winfrey. Reports are that her public persona (warm, charismatic, friendly) is genuine.

> working on the skills it would take to become a consultant

Curious to know what you were doing to make this happen. Did you have a guide?

Agree that on behavioral front, there is not much new signal after a few hours of conversation. Some evidence for this is a well-known study [1]. Unsure if the study has been validated, but the idea has stuck with me.

Regarding cognitive decay: I like to think time is simply pruning the weak and lazy neurons. At least that's what my remaining grey matter is telling itself. Your comment made me laugh. Thanks.

[1] https://kevin.burke.dev/pdf/30_seconds_teacher_quality.pdf

Yes to this! However, a while ago I tested OSQP, but I found low precision and the speed didn't justify using it over other solvers. The project remains on my radar due to the strength of the development team -- amazing group -- and knowing that numerical solvers can really shine in one domain while they struggle in others.

Another pair of projects that I find useful are ECOS [1] and CVXPY [2]. CVXPY makes head-to-head comparisons of solvers (including ECOS and OSQP) trivial.

[1] https://web.stanford.edu/~boyd/papers/ecos.html

[2] https://www.cvxpy.org

Agree that "rock made weak and ductile under intense heat and pressure" would probably be better than "liquid", but common language and daily experience do not apply. Whatever the dynamic processes are doing, they must have symmetry and be stable -- hot plumes of stuff don't punch through the contents in random locations.

Why is the earth's crust so stable? It has supported life for billions of years, yet it is just 12 mi thick [1] and floats on top of liquid rock that extends for thousands of miles -- the part of earth we are most familiar with is a tiny fraction of what our planet really is. You could imagine that turbulence in the liquid below would cause currents that blast through the crust with catastrophic consequences, and that this would happen regularly. But it doesn't -- we have a small number of (super) volcanos, but they are pretty rare. I find this unnerving and remarkable. Our perception of reality -- in this case the earth beneath us -- is skewed.

[1] https://en.wikipedia.org/wiki/Structure_of_Earth