Standard Error via the Central Limit Theorem

Marrow Mills fills sacks of flour, and years of records mean the plant is not guessing about its own machinery: the spread of individual sacks around the long-run average fill is known in kilograms. That figure describes the population of sacks as a whole, not any one batch.

Quality control does not weigh every sack. Each shift it pulls a fixed number of sacks at random and records their average weight. Naturally that shift average is never exactly the long-run figure - it lands a little above or below each time - and the supervisor wants to know how big those swings should normally be, so that a genuinely unusual shift can be told apart from ordinary variation. The plant runs several lines with different sampling plans, so both numbers come in with the job.

Task: Print the standard error of the sample mean for a shift of that size, in kilograms, rounded to 2 decimals. Round once, at the end.

Input

The first line holds the population standard deviation of individual sack weights in kilograms, a decimal number. The second line holds one integer, the number of sacks pulled per shift (at least 1).

Output

One line holding the standard error in kilograms, rounded to 2 decimals and printed the way Python prints a float - 1.7, never 1.70.

Example:

Input:
12
36

Output:
2.0