Hacker News: midjji

New comment by midjji in "Pattern of brain damage is pervasive in Navy SEALs who died by suicide"

midjji — Tue, 02 Jul 2024 07:38:43 +0000

HRT does not help with scarring or issues caused by scarring. Lots of other things sure, but not scarring, which is what the article claims is the problem.

New comment by midjji in "Pattern of brain damage is pervasive in Navy SEALs who died by suicide"

midjji — Tue, 02 Jul 2024 07:37:34 +0000

Its mostly a question of pr I think. More highly trained troops are less likely to die or mess up, and that is bad pr. So we train them more, but if that training also causes deaths, which becomes bad pr, we will train them less. Younger less trained soldiers are cheaper too, so I think the tradeoff will favor less training as soon as this becomes common knowledge anyways.

New comment by midjji in "Pattern of brain damage is pervasive in Navy SEALs who died by suicide"

midjji — Tue, 02 Jul 2024 07:35:20 +0000

The only usable EMPs we have are nuclear weapons. So if you are willing to use one, then just detonate it a bit closer to the surface and it will work just as well against everything we have.

New comment by midjji in "Pattern of brain damage is pervasive in Navy SEALs who died by suicide"

midjji — Tue, 02 Jul 2024 07:30:26 +0000

You say that suicide hotlines are more for the friends of people at risk, than the people at risk, as if it is a bad thing. We as a society generally dont know how to help people who consider suicide for its myriad of causes. We do know how to help their friends though, and pretending that there is help to be had is a big part of it.

New comment by midjji in "Pattern of brain damage is pervasive in Navy SEALs who died by suicide"

midjji — Tue, 02 Jul 2024 07:21:21 +0000

A .45 gunshot feels like a sound, and with hearing protections at a few meters you hear it but cant feel it in the slightest. You feel it in your hand but not further. Artillery is more similar to how it feels standing next to gigantic speakers at a concert, even with hearing protections you feel it throughout your entire body, except sharper.

New comment by midjji in "Pattern of brain damage is pervasive in Navy SEALs who died by suicide"

midjji — Tue, 02 Jul 2024 07:17:45 +0000

My bet is on a high threshold. There is no evidence for this at all in small arms enthusiasts.

New comment by midjji in "Pattern of brain damage is pervasive in Navy SEALs who died by suicide"

midjji — Tue, 02 Jul 2024 07:14:31 +0000

If this is accurate there is likely very little to be done, and the solution will likely be to have less training and retire a decade sooner.

New comment by midjji in "Pattern of brain damage is pervasive in Navy SEALs who died by suicide"

midjji — Tue, 02 Jul 2024 07:12:30 +0000

If I understood it right, its firing artillery, not rifles. Even then I doubt cavitation, but not shock echos at density borders tearing he tissues at the border.

New comment by midjji in "Why not parse `ls` and what to do instead"

midjji — Tue, 25 Jun 2024 19:31:10 +0000

The bash code which creates the c file which gets the list of null terminated files in a directory and compiles it, and runs it, is easier to write and understand. Bash is a lousy language to do anything in, python is almost always available, and if not, then CC is.

New comment by midjji in "Beyond velocity and acceleration: jerk, snap and higher derivatives (2016)"

midjji — Thu, 20 Jun 2024 06:30:56 +0000

Yes. The higher derivates are useful in many cases, both as sought properties and observations. The invariances implied by relativity,(the trivial notion that the universe behaves the same regardless of where you select the center), mean that most laws are defined on the second derivative. Taylor approximations are useful to approximate something locally, but properties of the system over wider regions generally need to account for the higher derivatives. You can see this in e.g. simulating a system over time requires that the derivatives at the borders of the valid taylor approximation region to be included as diracs.

Or in other words, you can approximate exp(x) as a set of first order taylor approximations that each covers a small window to arbitrary precision, but the combination of them is still has well defined higher derivatives that are not 0.

New comment by midjji in "Beyond velocity and acceleration: jerk, snap and higher derivatives (2016)"

midjji — Thu, 20 Jun 2024 06:17:16 +0000

Why bother with giving words for something which is longer than their mathemathical definition? The word can be unknown, but if the function is unknown the word is useless?

New comment by midjji in "Beyond velocity and acceleration: jerk, snap and higher derivatives (2016)"

midjji — Thu, 20 Jun 2024 06:12:10 +0000

But all of them are based on courses that define nth derivative, and the notion of continuous vs non continuous functions. What is the value of giving them names instead of just using nth derivative?

New comment by midjji in "Beyond velocity and acceleration: jerk, snap and higher derivatives (2016)"

midjji — Thu, 20 Jun 2024 06:10:29 +0000

What value is gained from using these terms over just saying nth derivative?

New comment by midjji in "Why camera calibration is so important in computer vision"

midjji — Sun, 09 Jun 2024 03:00:37 +0000

Will do

New comment by midjji in "Fighting an anti-doping finding"

midjji — Sun, 09 Jun 2024 02:52:06 +0000

It would be amazing. Willing test subjects and the competitions to actually tell if we are making improvements.

New comment by midjji in "Why camera calibration is so important in computer vision"

midjji — Wed, 05 Jun 2024 13:49:56 +0000

Its just for fun.

New comment by midjji in "Why camera calibration is so important in computer vision"

midjji — Wed, 05 Jun 2024 13:48:24 +0000

This is correct. They were calibrated in advance.

New comment by midjji in "Why camera calibration is so important in computer vision"

midjji — Wed, 05 Jun 2024 13:47:24 +0000

Very few know the old methods. Few were trained, and deep learning started working just 5 years after classics got good and easy enough that a few people could use it for a product. So there was never the needed wave of non deep learning based computer vision students, they all went to deep learning.

Many of the libraries are not well maintained. I needed a basic homography estimation recently and asked a minion to try opencv for it before anything more advanced. He got it working, but the best inlier ratio for every feature he tried with default parameters was 3%. The images were offset by 30 pixels left right, and less than a pixel in warp and taken with different exposure time. So he used sift...

He argued he got it working and that was as good as keypoint matching got... If I hadnt happened to be the guy who needed it one step up, it could have just propagated, someone adding a shitty ekf to make it smooth, then buried in layers of heuristics and api.

New comment by midjji in "Why camera calibration is so important in computer vision"

midjji — Wed, 05 Jun 2024 13:36:47 +0000

Fair enough, it is very hard to be worse than opencv in terms of user friendlyness, so even if it did nothing more than improve that, its better.

New comment by midjji in "Why camera calibration is so important in computer vision"

midjji — Wed, 05 Jun 2024 13:34:47 +0000

If you have say a 1Mpixel image which has been rectified, then a human will generally say that it looks correct if the calibration errors are less than 10 pixels. Perhaps as little as 5 if they know what they should look for and are careful. Most camera calibrations are this bad and no one notices. If showing a person the image rectified is the goal thats good enough. But, if you want to do post processing of any kind...

Sfm tests the calibration to the extreme and even single pixel calibration error will be highlighted as correlated reprojection error vector in most images. And that is assuming the bad calibration does not cause the system to fail outright. There is also the inbetween case where the system is only able to use small parts of the image.

The distortion field often visibly tells you if the estimation failed. It should be smooth, and monotonic, and you can draw it not just for where there are pixels but further and in higher resolution, and for regular lenses almost always highly symmetric. Looking at the distortion field you can see alot of problems that you could not otherwise. The most common problem is the monotonicity, since that constraint is very difficult to add to general optimizers. Since this means the distortion goes backwards, they are visible as sharp edges in the distortion field.