thetrainfold

Question about the algorithm:

Is the algorithm run from scratch each time a seam is removed? I.e. energy function computed again on the resized image, all seams recomputed, then only the lowest energy seam is removed.. and repeat.

Or are all the seams from the first calculation used for resizing?

Could it help to force long names, preventing simple, 'elegant' names?

Just imagine the work that haired people go through.. (sorry)

I was referring to this specific case. In this housing example, extra infrastructure is being built for the purpose of segregating people. In your examples the segregation emerges less deliberately.

My previous implied question is still unaddressed. I.e. will this kind of segregation actually spur them on in a positive way, or is a negative effect possible (e.g. lowered self esteem, etc.)?

(I work in marketing at scale)

Thank you.

(I don't really)

Remind them by segregating them from wealthier people?

Side note, but sharing salaries with colleagues is actually a good idea, due to the unfairness that can arise from salaries being kept secret.

Example article: https://www.nytimes.com/2018/08/31/smarter-living/pay-secrec...

"he noted that some combination locks allow for wiggle-room and if this one had a three-digit leeway, Mr Rosenthal put the chances at 1 in 8,000, "which is still a small chance"."

If this is the case, and there have been museum visitors having a go at opening the safe each day... this becomes a non story, right?

It would be unusual not to check the performance of the model at predicting the target variable, which would validate whether or not the derived feature is useful.