Coarse!

He is coarse in manner.

hinderer of good, guilty aggressor, coarse grained,

Her dress was made of coarse wool.

She is grown so brown and coarse.

We have a variable n which sets granularity.

We're just seeing the coarse scale geometry here.

Artificial gravity are ... ad lock cut primary coarse.

level of granularity, would be 12,500 less than that.

the coarse grained, and above all mean and ignoble

Her skin is coarse from years of working outdoors.

So we lose a little bit of granularity of information.

But he was mean and he was coarse and unrefined

It's coarse and rough and irritating... and it gets everywhere.

There are carp, tench and pike in the lake, for coarse fishing.

It is a very coarse sketch of how we migrated around the planet.

Experiments show that it's best to stick with a granularity you once have achieved, because then you won't have to track back through all the levels of granularity you already have seen.

We simply keep on working with these new reduced, simplified inputs and a decreased granularity.

You don't necessarily know with granularity exactly what organisms are providing what flavors and what characteristics.

In fact, it looks kind of funny and very coarse, but it does have less noise.

Soft wool is more expensive than coarse wool and both are superior to synthetics made of nylon.

We define the length of the individual subset by simply dividing the length of the entire input by occurring granularity.

We now need to increase the granularity but must make sure it doesn't get over the length of the total string.

And when you're squinting, you're turning that from fine grain to coarse grain, so you're reducing the quality of your data.

Neither removal of these subsets would fail, so now we have to increase the granularity by coming up with now four subsets.

And most granularly, I break them down by religion where there are 72 categories of religions in the world so an extreme level of granularity.

Removing any of these subsets will result in a passing test, which means that we again have to increase the granularity by doubling n in here.

And, in addition to the coarse scale geometry, they also used all of that detail to create a set of what are called displacement maps that animate as well.

It's trying to walk on the coarse terrain, sandy area, but depending on the moisture content or the grain size of the sand the foot's soil sinkage model changes.

The point is just that understanding tiny constant factors in the analysis is an inappropriate level of granularity for the kind of algorithm analysis we're going to be doing in this course.

Now we split the input into six subsets, resulting in six more attempts to remove individual parts all of them will pass and then we have to increase the granularity again, which would be 12.

We decrease the granularity by 1 but don't let it fall below 2, and we set the variable some complement is failing to true, because we need this to step out of this loop, which we do right away.

Say Call Him Allah or call Him Ar Rahman whatever the name you call Him by, all His names are beautiful. Do not say your prayers too loudly or in too low a voice, but follow a middle coarse.

And we basically capture enough information with video projector patterns that drape over the contours of her face, and different principle directions of light from the light stage, to figure out both the coarse scale and the fine scale detail of her face.

We split the input into initially two subsets, and if any of these subsets fails, as in here, we proceed with this very subset, otherwise, we simply go and increase the granularity, that is, we double the number of subsets and we go again to step one.

The second guiding principal is we're not going to focus on constant factors or lower returns, that would be inappropriate, given the level of granularity at which we're describing algorithms and third is where going to focus on the rate of growth of algorithms for large problem sizes.

What we do in this situation is we increase the granularity, that is, we take away smaller parts more frequently until we end up removing single characters such that we really, really get an input where every single character or every single part is relevant for reproducing the failure.