hasley

As I said, the far-right usually do not stop there.

One problem with "we only want the illegal or 'imcompatible' ones to leave the country" is that right-winged people tend to get extremer sooner or later. Other people will make them believe into more and more crude theories (like white people being more valuable than people of color) which leads to even more extreme actions (like murder). Hence, we have to say "no" to right-wing ideas as early as possible.

I think (relative) poverty is not an explanation on its own.

This is my take: Poverty makes people not vote for some parties. But making so many vote for a party, especially with unconstitutional goals, is the result of a lot of desinformation. The AfD uses any possible opportunity and any possible topic to tell people that Germany is rotten to the core - while it is truly one of the riches country in the world with mandatory health insurance and unemployment compensation. (Not saying that Germany does not have significant problems. Far from.) Then, they tell people whom to blame (refugees, jews, homosexuals etc.). This builds up hatred. it is not important whether anything of this is true at all. All that counts is: They can continue to be angry.

There are enough YouTubers who show AfD voters contradictions in their arguments. The AfD voters usually react with even more hate. They just want to see things burn while accepting any possible collateral damage.

The complete title is "Germany’s far-right AfD win historic victory, but fall short of majority in Saxony-Anhalt".

How can I change the title?

Apart from this, "winning" an election is not the same as actually becoming the government.

I feel so angry with and ashamed of my own country in a way I cannot even find words for.

Getting linear algebra in a programming language close to math formulas was, for a long time, my reason to use Octave.

When I first read about Julia, I was really amazed - especially the type system with its multiple dispatching and not automatically converting between types (e.g., between integers and floats). Though, I do not know, how Julia is today.

Today, I use Python instead of Octave (or Julia) - just because it has a large ecosystem and is widely adopted. An additional advantage is that Python has much better OOP features than Octave had back then.

However, I wished Julia had the status that Python has today.

Which open tools or libraries are currently the best to extract/remove vocals? Are there some that can even distinguish between speech and singing?

The Misspecified Cramér-Rao Bound is a minimum bound for the estimation performance (mean-squared error) when the assumed model of the estimator is not the real model.

Yeah. Somehow the rate of such and other cool, trippy topics seems to have declined on HN in favor of more and more AI topics.

Really interesting read, thank you!

One thing I found especially interesting: They argue that you can express the (complex) exponential function exp(x) as a power series with powers x^k. They do not say it explicitly, but if we assume the power series comes from a Taylor series, then the k-th factor 1/(k!) is the derivative evaluated at x=0. And the k-th derivative has exactly the unit needed to cancel the unit of x^k. So, all summands of the series are unitless and hence the exponential function's argument is unitless.

This argument would hold for any function which can be written as a series like this. I am wondering whether this is actually a "problem" of the derivative operator.

I basically agree, at least for standard functions like sin, cos, tan, exp etc. It is even possible to see mistakes in equations just by checking that all the units to standard functions cancel out making the arguments dimensionless.

On the other hand I am still unhappy with calling the ratio of two quantities, that happen to have the same units, "dimensionless". This way, you could create any two "dimensionless" quantities and try to compare them or use one in place of the other which might be meaningless.

The Cramér-Rao Bound (CRB) lets one compute the minimal mean-squared error that any unbiased estimator (algorithm) can achieve. Unfortunately, you need to know the probability density function (PDF) to compute the CRB. This work proposes a way to overcome this drawback. Maybe with this, the CRB gains more attention in the machine learning community - or even as much attention as it gets in the parameter estimation community.

Of you prefer an even more entertaining approach and a very gentle introduction into the topic, I recommend the comic "Logicomix" which tells Russel's journey (though not historically correct all the time for story telling reasons).

https://en.wikipedia.org/wiki/Logicomix

Today, far right-wing people in Germany use fraktur on shirts, cars etc. - even though it were the Nazis who abolished it.

One of the most exciting things about LLMs for me is to have someone with which I can discuss about technical/scientific/engineering things. Since usually I do not have a human at hand (or do not want to annoy people around me). Especially conversations that involve formulae can get really interesting and insightful.

Of course, you have to be careful with the answers. Especially when the discussions get longer. But usually I see that it is time to stop or to start a new session when the formulae do not make so much sense anymore or when the LLM repeats itself.

But with enough caution, LLMs are really a not-so-bad intellectual sparring partner for discussing ideas and insights.

But how does this gas or new gas get back into the liquid resulting in new pressure?

I am not doubting the theory of the gas getting out of the liquid.

I was just referring to the "urge" to crack my fingers. I thought, maybe this instant relaxation (with additional auditive feedback) results in an addiction which creates or increases my urge to crack.

I also crack my fingers in the morning, over the day, and before sleeping. I also feel this "tension".

But I am not sure whether it is a real "tension" or something my brain makes up to force me crack my knuckles which gives some perceived relief. Maybe I am just addicted to this relief.

I haven't seen any research on this urge to crack and the possible tension. Is this a physical tension, some psychologically perceived tension due to an addiction, or something else?

That is something I observed too:

When you build a tool (physical or software) or an API (web or library) it should be easy to use. The easier it is to use the higher the probability that people will use it. So, if people build a kind of rudimentary thing and say that is all you need (which may be technically correct), then they do not optimize for likelihood of adoption/usage.

I, as an engineer, shall work for a mission and not (so much) for money. But as soon as the mission can possibly be completed without me using the latest shiny AI tool, it is okay for the company to get rid of me.

Yes, it is a good fit when a customer is supposed to use some functionality one has implemented but shall not see the implementation.

In cases where I assume that enough test coverage exists, I simplify code that I am currently working on or which I need to read very often.

This way I have already replaced a lot of for-loops by range-based for-loops. It helps me to understand code faster.

But code parts that noone needs to touch or see do not need to be more readable.

A tool to record exact quotes of politicians with hashes and permalink - starting with the German far-right party AfD.

Interesting, thanks!

I suspect with "orthogonalization" they mean to find vectors that form an orthogonal bases (same subspace) for the vectors in the source matrix.

I wonder what would be the result if they used a matrix that is orthogonal and closest to the source matrix. Usually one uses the Frobenius norm (root of the sum of all squared matrix entries). Maybe, one could even try another norm that gives a sparser matrix.

Can't a system be DDoS'ed with wrongly signed JWTs as well?

Is signature checking (much) cheaper than finding an opaque session ID in a database?

The Cramér-Rao bound (CRB) is a fundamental bound in estimation theory. "Estimation" refers here to finding the parameters of a random process.

The CRB shows that for estimators (algorithms) that on average estimate a the correct value (unbiased estimators), there is a lower bound on the accuracy. Accuracy refers here to the variance (or equivalently to the mean squared error).

So, whatever fancy unbiased algorithm you will come up with, its average variance cannot be lower than the CRB.

So, the CRB tells you something about your measurement setup at hand. It can even show possibilities to improve estimation of certain parameters without being specific about the used algorithm.

The multi-parameter CRB gives a kind of "minimal" covariance matrix whose uncertainty ellipsoid is always inside the uncertainty ellipsoid of any unbiased multi-parameter estimator.

In PowerPoint, I am a happy user of IguanaTex [1].

There are also some other PlugIns like TeXsword. But I have not tested them. And newer MS Office versions seem to support Latex macros directly. [2]

[1] https://github.com/Jonathan-LeRoux/IguanaTex

[2] https://support.microsoft.com/en-us/office/linear-format-equ...

Electromagnetic waves can exist in vacuum. No (conductive) air is required.

Agree.

I am using type hints in Python as much as possible for my hand-coding. And it catches a lot of bugs (especially during code refactoring) that I would not have noticed so easily.